INV

ARIANT FACE RECOGNITION

IN A NETWORK OF CORTICAL COLUMNS

Philipp Wolfrum

Frankfurt Institute for Advanced Studies, JWG University, Ruth-Moufang-Str. 1, Frankfurt am Main, Germany

org L

ucke

Gatsby Unit, UCL, London, United Kingdom

Christoph von der Malsburg

Frankfurt Institute for Advanced Studies, JWG University, Ruth-Moufang-Str. 1, Frankfurt am Main, Germany

Keywords:

Face recogntion, Neural model, Recurrent network, Generative model, Dynamic links, Cortical column, Self-

organization.

Abstract:

We describe a neural network for invariant object recognition. The network is generative in the sense that it

explicitly represents both the recognized object and the extrinsic properties to which it is invariant (especially

object position). The model is biologically plausible, being formulated as a neuronal system composed of

cortical columns. At the same time it has competitive face recognition performance.

1 INTRODUCTION

An impressive capability of our visual system is in-

variant object recognition. The same object seen at

different position, distance, or under rotation leads to

entirely different retinal images which have to be per-

ceived as the same object. In short, the system has to

be invariant in terms of these transformations.

The traditional approach to achieving invariance

in a neural system is the use of feature hierarchies.

This idea was ﬁrst expressed by Frank Rosenblatt in

his four-layer perceptron (Rosenblatt, 1961) and a

multitude of similar models has followed since (e.g.

(Fukushima et al., 1983; Riesenhuber and Poggio,

2003)). Feature hierarchies consist of a number of

stages that combine simpler features into more and

more complicated ones while at the same time pool-

ing over position, scale, etc. in order to achieve in-

variance to these transformations. This leads to a po-

tential weakness of the concept, the inability to distin-

guish patterns that contain the same features in differ-

ent arrangement, an ambiguity that is especially likely

to occur in scenes with complex background. While

this problem has been partially solved by newer mod-

els, one property of feature hierarchies remains: By

pooling over variances, they do not only become in-

variant to them, but they effectively discard this infor-

mation. In consequence, a system of this kind may

be able to detect and recognize objects, but it has no

way of telling where the object is, what size it has,

whether the person just recognized has a happy or a

sad expression on her face, etc.

Our visual system is deﬁnitely able to perceive

these extrinsic properties in addition to identifying an

object. There is even widespread belief (e.g., (Xu,

1993)) that it has the ability to reconstruct the at-

tended parts of a scene from an internal representa-

tion. Only such ability to accurately reconstruct gives

ultimate assurance that all relevant aspects of the im-

age have been understood and represented correctly.

This is related to a distinction in computer vision,

where generative models represent the joint proba-

bility distribution of input data and recognized ob-

ject explicitly, while discriminative models only use

the posterior probability of the object given input data

(Ulusoy and Bishop, 2005).

We here propose a novel model of object recogni-

tion that follows this spirit of explicitly representing

extrinsic properties and reconstructing the perceived

object. Information about where the object of inter-

est is located in the visual ﬁeld is represented by dy-

namic links (a concept ﬁrst introduced in (von der

Wolfrum P., Lücke J. and von der Malsburg C. (2008).

INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 38-45

DOI: 10.5220/0001073300380045

 SciTePress

Malsburg, 1981)) that control information ﬂow be-

tween the input domain and an invariant “Assembly”

window in the model (see Fig. 1). At the same time,

the Assembly receives top-down input from the model

“Gallery”, and uses this information to try to recon-

struct the perceived object. In this way, the whole

system implicitly reconstructs the full image of the

perceived object, representing its intrinsic properties

in the assembly window and the extrinsic properties

(position, deformation) in the dynamic links.

Figure 1: The principle of our reconstructive model for ob-

ject recognition. See text for details.

We apply this mechanism here to face recognition

as example of object recognition. Sect. 2 introduces

the smallest computational elements that make up our

model, which we then proceed to describe in detail

in Sect. 3. We analyze performance of the system in

Sect. 4 and close the paper with a discussion.

2 THE BASIC COMPUTATIONAL

UNITS: CORTICAL COLUMNS

Our model is neurally implemented as a network of

cortical columns. Today it is widely accepted that

columns are basic computational units of the brain

(Mountcastle, 1997). Columns in turn consist of mini-

columns, which are bundles of ≈ 100 strongly inter-

connected cortical neurons.

All neurons of a minicolumnar network represent

one single feature by their average ﬁring rate x, which

we call the unit’s activity. It has been shown that the

average spike rate of large excitatorily coupled neuron

populations can be realistically described by continu-

ous variables (van Vreeswijk and Sompolinsky, 1998)

even on a fast timescale, and a speciﬁc minicolumn

model has been proposed in (L

ucke and von der Mals-

burg, 2004).

Several cortical minicolumns constitute a

(macro)column (also called “hypercolumn” (Hubel

and Wiesel, 1977) or “segregate” (Favorov and Dia-

mond, 1990)), a group of minicolumns that together

represent all relevant features at a certain point. The

different features represented by a macrocolumn can

compete through mutual inhibition of its constituing

minicolumns (see below).

We describe column activity by a set of continuous

differential equations called modiﬁed evolution equa-

tion. The activity of the ith minicolumn in a macro-

column of K minicolumns is given by

˙x

= x

−x

∑

j=1

. (1)

The parameter ν introduces competition among the

minicolumns forming a macrocolumn. For ν = 0,

there is no competition. All minicolumns represent

their input proportionally, while the interaction term

∑

j=1

normalizes the steady state macrocolumn

activity to a 2-norm of 1.

For ν = 1, we have strong competition among the

minicolumns, leading to winner-take-all (WTA) be-

havior. To see this, regroup Eq. 1 (with ν = 1):

˙x

= x

−

∑

j=1

In this case the 1-norm of the column activity

∑

is equal to 1 for the steady state, so the interaction

term

∑

j=1

here is the average activity-weighted

input to the macrocolumn. This means that only those

minicolumn activities grow whose input is higher than

this weighted mean input to the macrocolumn, other-

wise they shrink. This lets the weighted input aver-

age grow, because the bias shifts towards strong in-

puts. Eventually, all minicolumn activities decrease

to 0 except for the minicolumn with the strongest in-

put, whose activity approaches 1.

In our model of object recognition we assume that

there are two types of columns with different func-

tions. Dynamically, they only differ in the use of the

competition parameter ν:

• Feature columns represent their input in a linear

fashion. Consequently, the minicolumns in a fea-

ture column have no need to compete among each

other, i.e. for them the parameter ν = 0.

• Decision columns show a WTA behavior leading

towards a state where only the minicolumn getting

the strongest input remains active. These columns

receive a ν-signal that cyclically rises from 0 to 1.

So they start out with linear dynamics like feature

units. With rising ν, competition sets in, leading

to an ever stronger WTA behavior that leaves only

the minicolumn with the strongest input active.

In the networks that we will introduce in Sect. 3, mini-

columns communicate with minicolumns of other

macrocolumns. For this communication, a macrocol-

umn scales the output activities of its K minicolumns

INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS

such that its output energy stays constant:

i,out

∑

j=1

. (2)

3 THE MODEL

Our network is made up of layers, which loosely cor-

respond to the different cortical areas that make up

the ventral stream (the object recognition system of

the brain). Layers are organized topologically, with a

topology that may correspond to that of the retina or

to a more abstract space. The layers of our network

interact and activity collectively converges towards a

ﬁnal state that represents the “percept” of the network,

in our case the possible recognition of a face.

Layers may contain both feature and decision

columns. If we assume every feature column to rep-

resent all relevant features in one position of a retinal

image, then layers of feature columns can represent

whole images. The network introduced below uses

layers of two different spatial arrangements:

• Rectangular grid: Straightforward representation

suitable for any image. Every column represents

one speciﬁc geometric location (see Fig. 2(a)).

• Face graph structure: An arrangement speciﬁcally

suited for faces, where each macrocolumn repre-

sents an important landmark position on a face

(see Fig. 2(b)). Note that in this case, a macrocol-

umn does not necessarily represent a ﬁxed spatial

location in the image, but rather a ﬁxed semantic

location (nose, mouth, eye, chin, etc.). Geomet-

ric locations of landmarks can change according

to the face they represent.

(a) Rectangular grid (b) Face graph

Figure 2: Different representations of images.

The network consists of columns organized in the fol-

lowing layers (see Fig. 3):

• Input Layer: Represents the input image in a rect-

angular grid of P = 20 ×20 points.

Figure 3: Architecture of our network. The gray oval struc-

tures represent macrocolumns, with minicolumns as lighter

cylinders inside. The numbers of mini- and macrocolumns

shown here are chosen exemplarily for visualization pur-

poses only and are not identical to the real numbers of units

used in this work. Input Layer is organized in a rectan-

gular grid (represented by the red lines connecting macro-

columns), while both Assembly Layer and Gallery Layer

have a face graph topology. Input and Assembly are con-

nected all-to-all (shown exemplarily for the left-lowermost

point in Assembly Layer), while Assembly landmarks are

connected only to the same landmarks in Gallery, but to all

identity minicolumns there. The green lines connecting the

three layers represent a possible ﬁnal state of the network.

• Assembly Layer: Contains intermediate informa-

tion from both the input image (represented in the

Input Assembly macrocolumns) and the gallery

(represented by the Gallery Assembly units).

• Gallery Layer: Represents all gallery face images

in a face graph of macrocolumns.

The following three subsections describe these layers

in detail.

3.1 Input Layer

The Input Layer represents the input image in a rect-

angular grid of P = 20 ×20 points. At each grid

point a macrocolumn represents K = 40 Gabor fea-

tures (Daugman, 1980) extracted from the image at

that position, namely wavelets of 8 orientations and

5 scales. The minicolumns x

being an index for

the input layer colums) in this layer are feature units,

i.e. they linearly represent the input they are getting

from the image:

˙x

= Gabor

−x

∑

j=1

Gabor

, (3)

for every Gabor feature i and every position p on the

input grid.

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

3.2 Assembly Layer

The Assembly layer contains intermediate informa-

tion from both the input image (represented in the

Input Assembly macrocolumns and the gallery (rep-

resented by the Gallery Assembly units). This infor-

mation is organized in an average face graph arrange-

ment with Q = 48 landmarks (see Fig. 2(b)). The units

of Input Assembly and Gallery Assembly are feature

units. Mathematically, the input to an Input Assembly

unit at position q of the face graph is given by

I A

√

∑

p=1

p,q

out

i,out

, (4)

with x

p,q

out

the output strength of the dynamic link (see

below) controlling the ﬂow between Input column I

and Input Assembly column I A

The input to a Gallery Assembly unit at position q

is the weighted superposition of all Gallery activities

at the same landmark, ﬁltered/multiplied by the fea-

ture vector represented by that respective landmark:

√

∑

m=1

m,out

i,m,eff

, (5)

with the “efferent weight” w

i,m,eff

representing the

strength of Gabor feature i in landmark q of Gallery

image m (of M in total).

The Assembly Layer contains the control units

mentioned above, which mediate the signal coming

in from Input Layer. These control units provide po-

tential connections (dynamic links) between every In-

put Layer point to every point in the Input Assem-

bly. The activity of the control units is driven by the

feature similarity of the corresponding points in In-

put Layer and Gallery Assembly and in the end rep-

resents a position-invariant match between Input and

Assembly Layer. Additionally, there is mutual sup-

port of control units that together would represent a

geometrically consistent match between Input Layer

and Input Assembly (see Sect. 3.2.1 for details.). The

dynamic links are decision units, all links at one posi-

tion of the Assembly Layer pointing to different Input

Layer positions forming one decision column. The in-

put to a dynamic link x

p,q

connecting input position p

and assembly position q is given by the scalar product

between both macrocolumn outputs and the topologi-

cal inﬂuence from its neighbors:

p,q

∑

j=1

j,out

I A

j,out

+ c

top,C

∑

˜p, ˜q

top

(p, q, ˜p, ˜q), (6)

where c

top,C

deﬁnes the strength of topological inter-

action (see Sect. 3.2.1).

3.2.1 Topological Cooperation Among Control

Units

As mentioned before, there is topological cooperation

among the control units of the Assembly Layer. The

purpose of this cooperation is to establish a reason-

able match between the different geometries of Input

and Input Assembly. A given dynamic link connects

a speciﬁc macrocolumn A of the Input Layer with an-

other macrocolumn B of the Input Assembly. Due to

the geometry of both layers, both macrocolumns rep-

resent distinct positions p

and p

in retinal coordi-

nates and internal image representation space, respec-

tively. Consequently, the dynamic link between them

represents a certain geometric distance d

= p

−p

The idea is now to have topological connections

in order to support parallel or near-parallel dynamic

links. Therefore we deﬁne the strength of a topologi-

cal connection between any two dynamic links i and j

whose macrocolumns are neighbors in the face graph

through a monotonically decreasing function of their

non-parallelity/disparity:

i j

= f (kd

−d

) (7)

Here we use a linearly decreasing thresholded func-

tion of the form

f (x) = max(0, 1 −βx) (8)

Thus topological interaction is always positive and

acts only between more or less parallel (depending on

β) neighboring links.

3.3 Gallery Layer

The Gallery Layer represents all M gallery face

images in a face graph of macrocolumns. Each

macrocolumn corresponds to one landmark, with

the minicolumns representing speciﬁc feature vec-

tors for the individual faces at the respective land-

marks. The units in Input Assembly activate the

Gallery minicolumns through receptive ﬁelds repre-

senting the stored facial landmark features, activating

more strongly units of faces that are similar to the nor-

malized input image in Input Assembly:

∑

i=1

i,m,aff

I A

i,out

top,G

k N(q) k

∑

˜q∈N(q)

˜q

m,out

, (9)

with c

top,G

deﬁning how strongly Gallery units repre-

senting the same face in a neighborhood N(q) around

landmark q cooperate. Since Gallery columns are de-

cision columns, this process leaves only the correctly

recognized identity active in the end.

The Gallery projects a weighted superposition of

its stored faces to Gallery Assembly. Point-to-point

INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS

comparison with the Input Assembly and competition

among stored models leaves only the correctly recog-

nized identity active in the end.

3.4 System Behavior

Figure 4: The principle of information processing in our

system. Full arrows denote ﬂow of information from one

area to another. At the diamond symbols, information com-

ing in via dashed lines is compared (scalar product). From

there, a similarity signal ﬂows out via the thin dotted lines,

modulating point by point the information ﬂow between ar-

eas.

We simulate the network dynamics using the simple

Euler method, however, the time step iss adapted dy-

namically to the average change of activity in the net-

work in order to keep the system stable. All mini-

columns have a small, but non-zero initial activity

x(0) = 0.01. We drive the two decision layers with

slightly different ν dynamics: ν

Topology

rises from

0.25 to 0.9 during the cycle, while ν

Gallery

starts out

from 0, also rising to 0.9. Consequently, competition

and decision set in earlier in the control units than in

the Gallery. The ﬁnal value ν

max

= 0.9 makes the de-

cisions of the network less sharp than a ﬁnal value of

The way the network processes information is

sketched in Fig. 4 The units in the Input Layer, which

receive input directly from the incoming image (not

shown in Fig. 4), quickly develop to a state where

they represent the input image via the different Gabor

feature values at all grid positions. This information

ﬂows to the Input Assembly modulated by the activi-

ties of the control units connecting every point in In-

put Layer with every point in Input Assembly. The

control units in turn are driven by the similarity of In-

put and Gallery Assembly at that pair of positions that

they control.

The image information in Input Assembly in turn

acts as input to the Gallery units, where it gets ﬁl-

tered through the individual receptive ﬁelds of the

minicolumns, exciting those units more that represent

faces more similar to the input image. A superposi-

tion of all stored faces, weighted by the current activ-

ity of the Gallery units, then ﬂows to Gallery Assem-

bly. This is equivalent to having a non-weighted ﬂow

of all stored faces from Gallery to Gallery Assembly,

but modulated by the similarity of the representation

in Input Assembly and each respective Gallery face

(see 4).

The time course of the recognition process is visu-

alized in Figs. 5 and 6. Since initially all control units

have equal activity, this leads to a superposition of

image information from all Input points at each Input

Assembly location, resulting in a feature-less, more or

less homogeneous mix of visual information in Input

Assembly (ﬁrst image in Fig. 5). In Gallery Layer,

all faces are equally active initially. Gallery Assem-

bly, which receives equal input from all Gallery units,

will therefore initially receive a superposition of all

Gallery faces, resembling an “average face” (like the

ﬁrst image in Fig. 6). The control units are driven

by the similarity of the information stored in their

dedicated feature units. Therefore control units that

connect points of the average face with similar Input

points will become stronger, while control units rep-

resenting irrelevant matches will be weakened. Since

the information ﬂow from Input Layer to Input As-

sembly is modulated by the control units, the image

in Input Assembly starts to develop from a gray non-

descript superposition to a more and more clear ver-

sion of the input image. It may be shifted and possibly

distorted such that it conforms to the topology of the

face graph of Gallery Assembly. This development

is shown in Fig. 5. Due to competition between the

minicolumns of each Gallery macrocolumn and co-

operation among minicolumns of different landmarks

representing the same face the Gallery will start to fa-

vor some of the stored faces over others. This in turn

changes the image in Gallery Assembly from an av-

erage face to a superposition that is biased towards

one or several of the better ﬁtting gallery faces (sec-

ond and third image of Fig. 6). This sharpened tar-

get face now helps at positioning the normalized input

image even more precisely. In the ﬁnal state, the Input

Assembly will contain a shifted and maybe distorted

version of the input image, while in Gallery the mini-

columns of only one face are still active, and Gallery

Assembly contains a copy of that face of the Gallery

that the system judges to be most similar to the input

image.

4 TEST RESULTS

We tested our system on the FERET (Phillips et al.,

1998) and the AR (Martinez and Benavente, 1998)

databases, two very popular databases for the evalu-

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

Figure 5: The process of ﬁnding the correct mapping be-

tween Input Layer and Input Assembly. The three images

show the nonchanging input image on the left side, and an

image reconstructed from the activities of the 48 landmarks

of the Input Assembly on the right side. This initially (top

image) contains a superposition of all input information, re-

sulting in a gray more or less uniform image. This image

then differentiates towards a normalized (i.e. shifted and de-

formed if necessary) version of the input image. This pro-

cess is driven by the control units, which are represented

here by lines connecting the input image with the Input

Assembly. Each line represents the “center of mass” of a

control macrocolumn, i.e. the location in the input image

where its minicolumns are pointing to as a group, weighted

by their activity.

ation of face recognition systems. We followed the

testing protocols of (Phillips et al., 2000) (the ofﬁ-

cial FERET evaluation) and of (Tan et al., 2005). The

FERET database contains images of 1196 individu-

als, while the subsets of the AR database used in (Tan

et al., 2005) and by us contain 100 faces. In order to

test the performance of a face recognition system, it

is confronted with a gallery of images of all faces in

the database, and is then asked to identify a different

set of images containing pictures of (possibly a subset

of) the faces in the gallery photographed under differ-

ent conditions. Often not only the best match cho-

sen by the system is recorded, but also the follow-up

matches. This allows to construct cumulative match

scores, the match score of rank n representing the

fraction of test images whose correct match appears

among the n best matches found by the system.

From the FERET database we used the following

testing subsets: The set

fafb

contains photographs of

1195 individuals taken on the same day as the gallery

images, but with the subjects showing a different fa-

Figure 6: Time course of the image represented in Gallery

Assembly (from left to right). The Gallery Assembly gets

input from all Gallery units and thus contains an activity

weighted average of all faces in the gallery. Initially, when

all Gallery units are equally active, this weighted average is

a real average of all gallery faces, i.e. a mean face like in the

leftmost image. With ongoing dynamics and rising compe-

tition, it gets biased towards the better ﬁtting gallery faces,

and ﬁnally contains only a representation of the image the

system has recognized.

cial expression. The set

Duplicate I

contains 722 of

images that were taken at least one day but less than

18 months after the gallery images. Finally, the set

Duplicate II

contains 234 images taken more than

18 months after the gallery images. The cumula-

tive match scores of our system for this database are

shown in Fig. 7.

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

5 10 15 20 25 30

Cummulative match score

Rank

fafb

Duplicate I

Duplicate II

Figure 7: Performance of our system on the FERET

database.

The AR Face Database contains several testing sub-

sets with images of the same 100 subjects that make

up the gallery. The subsets b, c, and d contain images

of the subjects smiling, expression anger, or scream-

ing, respectively. While those subsets were taken on

the same day as the gallery images, the subsets h, i,

and j show the subjects at a later session expression

those same three emotions. Subsets e and f show sub-

jects wearing sunglasses and scarfs, and subsets k and

l show the same situation at a later date. Cumulative

match scores for this database are shown in Fig. 8.

Table 1 shows the performance of our system con-

sidering only the best match (i.e. rank 1), and com-

pares it to the recognition rates of the systems eval-

INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS

Table 1: Rank 1 match scores (in %) of our system, compared to those reported in the literature. The middle column shows

the scores for the best performing system (Wiskott et al., 1997) of the ofﬁcial FERET evaluation, and in brackets the average

score of all 13 systems evaluated. The next column shows the performance of the two systems (SOM-Face/LocPb) proposed

in (Tan et al., 2005). The probe sets from the FERET database are the same as those of Fig. 7, while for the AR database, the

three emotion sets (b,c,d and h,i,j, respectively) and the two types of occlusion (e,f and k,l) have been averaged.

our system (Phillips et al., 2000) (Tan et al., 2005)

fafb 95 95 (85) 92/-

FERET duplicate I 47 59 (40)

duplicate II 26 52 (22)

Emotion 91 95/82

AR Em. duplicate 61 81/82

Occlusion 73 96/81

Occ. duplicate 36 56/51

0.5

0.6

0.7

0.8

0.9

5 10 15 20 25 30

Cummulative match score

Rank

(b)

(c)

(d)

(h)

(i)

(j)

(a) Results for the emotion datasets.

0.3

0.4

0.5

0.6

0.7

0.8

0.9

5 10 15 20 25 30

Cummulative match score

Rank

(e)

(f)

(k)

(l)

(b) Results for the occlusion datasets.

Figure 8: Performance of our system on the AR database.

uated in (Phillips et al., 2000), and to the perfor-

mance of (Tan et al., 2005). We can see that our

system outperforms the average of the systems tested

in the FERET evaluation, but does not reach the per-

formance of the winner of this evaluation. Similarly,

performance on the AR database is poorer than that

of the better approach proposed in (Tan et al., 2005).

We can conclude that while our system is deﬁ-

nitely competitive, its performance does not reach that

of current top-notch face recognition systems. How-

ever, we did not apply any parameter tuning to the

system as tested here. For example, it turns out that

performance of the model grows monotonously with

input grid size, with our resolution of 20x20 points

still being far from saturation. In fact, the winner

of the FERET evaluation (Wiskott et al., 1997) uses

Gabor wavelets from every pixel of the input image!

Other parameters that could be adjusted include the

relative contribution of different landmarks, or the

strength of topological interaction among the control

units (Sect. 3.2.1).

5 DISCUSSION

Our main motivation behind this work was to create a

fully neural and biologically plausible model, which

is why we did not pay more attention to parameter

tuning. The nature of our approach of explicitly using

neural elements to route information enables a sys-

tem that performs object detection and recognition in

one single network. This is in contrast to most other

neurally inspired object recognition approaches, (in-

cluding the approaches in (Wiskott et al., 1997) and

(Tan et al., 2005)), which usually have to fall back on

some algorithmic shortcuts to close the gap between

the detection/segmentation and the recognition sub-

systems.

The price we have to pay for such a system is

its enormous computational cost. The strong depen-

dence of this cost on input grid size is the reason why

we constrained ourselves to a sub-optimal resolution

of 20x20 points. However, we are workingon solving

this problem by spreading the routing from input to

assembly over several layers (Wolfrum and von der

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

Malsburg, 2007). Also, our current system is only

invariant to translation and slight deformation of im-

ages. Work is under way to also address other trans-

formations like rotation and scaling.

On the other hand, a fully neural system with most

parameters represented locally naturally allows for

adaptation through learning. Therefore we refrained

from hand-tuning such local parameters here, but will

address this issue through learning in future work.

In that sense, the system presented here marks

but a starting point from which we could develop a

fully neural version of a generative vision model that

does not throw away variance information, but retains

and uses it for recognition through active information

routing. We are convinced that also for technical sys-

tems this appraoch to vision can serve as an inspi-

ration. The impending transition to massively paral-

lel processor arrays will revive interest in data ﬂow

architectures, in which data arrive just in time over

dedicated pathways on processing nodes. Studying

how these mechanisms work in the brain may turn out

fruitful for designing robust and autonomous parallel

computing systems.

ACKNOWLEDGEMENTS

We thank Urs Bergmann for help in programming,

and Alexander Heinrichs for helping to preprocess the

database images. This work was supported by the

European Union through project FP6-2005-015803

(“Daisy”) and by the Hertie Foundation.

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INVARIANT FACE RECOGNITION IN A NETWORK OF CORTICAL COLUMNS