Gender Recognition using Hog with Maximized Inter-Class

Difference

M. E. Yildirim

, O. F. Ince

, Y. B. Salman

, J. Kwan Song

, J. Sik Park

and B. Woo Yoon

Department of Electrical Engineering, Bahcesehir University, Istanbul, Turkey

Department of Electronics Engineering, Kyungsung University, Busan, South Korea

Department of Software Engineering, Bahcesehir University, Istanbul, Turkey

Keywords: Gender Recognition, Random Forest, Histogram of Oriented Gradients, Inter-Class Difference, Adaboost.

Abstract: Several methods and features have been proposed for gender recognition problem. Histogram of oriented

gradients (Hog) is a widely used feature in image processing. This study proposes a gender recognition

method using full body features. Human body from side and front view were represented by Hog. Using all

bins in the histogram requires longer time for training. In order to decrease the computation time, descriptor

size should be decreased. Inter-class difference was obtained as a vector and sorted in a descending order.

The bins with the largest value were selected among this vector. Random forest and Adaboost methods were

used for the recognition. As a result of both tests, the classifier using first 100 bins with maximum

difference gives the optimum performance in terms of accuracy rate and computation time. Although

Adaboost performed faster, the accuracy of random forest is higher in full body gender recognition.

1 INTRODUCTION

Object recognition is an important and interesting

subject of computer vision. An object recognition

system finds objects in the real world from an

image, using object models which are known a

priori. A human can easily distinguish between a

male and female (Bruce, 1993). However,

recognizing a gender by a smart system is a

challenging task. It is an essential field that can be

applied for surveillance systems, medical purposes,

content based indexing, biometrics, demographic

collection, targeted advertising and human computer

interaction.

Most of the existing approaches for gender

recognition rely only on facial features (Alexandre,

2010; Wu et al., 2011; Wu et al., 2010; Makinen and

Raisamo, 2008). These studies were generally

applied to standard databases having high resolution

aligned frontal faces. However, people can appear in

different scales and viewpoints in real-world images

(Khan et al., 2014). In real time conditions, where

videos are taken by a Closed Circuit

Television(CCTV) system, capturing face with

enough details to extract features on it can`t be

satisfying. CCTV cameras operating for security are

mostly located in quite far distance from people.

Recently, Zhang et al., (2013) proposed two

pose-normalized descriptors based on deformable

part models for attribute description. Ng et al.,

(2013) obtained 80.4% accuracy rate by presenting a

gender recognition system based on convolutional

neural network (CNN) on color images, which

automatically learns the most informative features

during training. Bourdev et al., (2011) achieved

approximately 82.4% accuracy by employing

poselets that represent small parts of the body under

a specific pose, to recognize several attributes

including gender.

Significant aspects of recognition are feature

selection and extraction. Performance and accuracy

of the system can be increased by using proper

features which should conform to several criterias

such as uniqueness, performance, collectability,

acceptability and circumvention (Gou et al., 2012;

Hossain and Chetty, 2011; Yildirim et al., 2014).

The recognition system should have high

accuracy rate as well as low processing time and

computation load. The accuracy rate can be low due

to real world conditions such as poses, clothing style

and color, occlusion and shadows. There are only a

108

Yildirim, M., Ince, O., Salman, Y., Song, J., Park, J. and Yoon, B.

Gender Recognition using Hog with Maximized Inter-Class Difference.

DOI: 10.5220/0005715401060109

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP, pages 108-111

ISBN: 978-989-758-175-5

few studies found in literature on body based gender

recognition due to such conditions (Ng et al., 2012).

This study proposes a gender recognition method

using full body features. Dataset for this work has

been created from the CCTV videos taken at random

times of the day at certain cameras. People in the

vidoes are extracted and represented by Hog.

A hog descriptor has large number of bins.

Thus, can cause much computation time for training

and testing. One method to increase the processing

speed is decreasing the descriptor size. The

important point is that, accuracy should stay

sufficient while the computation speed is increased.

In this sense, we aimed to select the bins that gives

the most distinguishing information on classes.

This paper is organized as follows. In section 2,

we briefly mention about Hog feature. In section 3,

random forest method is explained. In section 4,

gender recognition is given with the description of

datasets and experiments.

2 HISTOGRAM OF ORIENTED

GRADIENTS

Dalal and Triggs (2005) proposed Hog algorithm.

The basic idea is that local object appearance and

shape can often be characterized rather well by the

distribution of local intensity gradients or edge

directions, even without precise knowledge of the

corresponding gradient or edge positions (Dalal and

Triggs, 2005).

The computation procedure is as follows: to

obtain a gradient image, a mask with [-1, 0, 1],

without smoothing is applied to the image. For the

computation of the Hog descriptor the gradient

image is divided into 16x16 pixel non-overlapping

blocks of four 8x8 pixel cells. After calculating the

gradients, they are mapped into 9 bins within a range

of 0◦−180◦.

Hog has become popular for various problems of

pattern recognition. Collins et al. (2009) developed a

descriptor named PixelHOG which was found to

perform better than other descriptors based on

Pyramid Hog (Bosch et al., 2007) and Pyramid of

Words (Lazebnik et al., 2006). Bourdev et al.,

(2011) combined color histogram, Hog and skin

features to represent poselets to gather gender

information to make it robust against pose and

occlusion.

3 CLASSIFICATION METHODS

3.1 Random Forest

A random forest multiclass classifier consists of a

number of trees, with each tree using some form of

randomization. The leaf nodes of all trees are labeled

by estimations of the posterior distribution over the

image classes. Each internal node contains a test that

best splits the space of data to be classified. An

image is classified by sending it down each tree and

aggregating the reached leaf distributions.

Randomness can be injected at two points during

training: in subsampling the training data so that

each tree is grown using a different subset; and in

selecting the node tests.

The trees here are binary and are constructed in a

top-down manner. The binary test at each node can

be chosen in one of two ways: (i) randomly, for

example data independent; or (ii) by a greedy

algorithm which picks the test that best separates the

given training examples. Best here is measured by

the information gain

∆  















(1)

caused by partitioning the set  of examples into

two subsets 



according the given test. Here is

the entropy 

∑



















with 



the

proportion of examples in  belonging to class , and

|.| the size of the set. The process of selecting a test

is repeated for each nonterminal node, using only the

training examples falling in that node. The recursion

is stopped when the node receives too few examples

or when it reaches a given depth (Bosch et al. ,

2007

A test image is passed down through each

random tree until it reaches a leaf node. All the

posterior probabilities are then averaged and the

argmax is taken as the classification result of the

image.

3.2 Adaboost

Boosting is a well known statistical method that uses

the original distribution of positive and negative

examples to compute simple rules also called weak

classifiers and combines them to create a stronger

classifier. AdaBoost is the most commonly used for

binary classification, but it can also handle multiple

classes with minor modifications (Freund and

Schapire, 1996).

Gender Recognition using Hog with Maximized Inter-Class Difference

109

4 GENDER RECOGNITION

4.1 Dataset

In this study, we established and used our own

database for gender recognition. The images were

captured from the CCTV videos recorded around the

campus of a university in Korea. In the captured

images, only the pedestrians which are in the range

of 15 meters from both sides and 7 meters ahead of

the camera. The camera is stationary and standing at

a height of 6 meters from the ground. There are 700

samples in each class for training. Each class

includes 100 different people with 7 different poses

for each person. Figure 1 shows samples from the

pedestrian dataset, in which first two images are

female and the other two are male samples.

Figure 1: Sample images from the used dataset.

4.2 Experiments and Results

The size of training images was 48x96 pixels. Each

image was divided into 55 blocks with 50% overlap.

Every block had 4 cells with 2x2 configuration and

each cell is represented by 9 gradient bins. As a

result, descriptor has 1980 bins per image in

histogram.

The tests were conducted in WEKA 3.6 tool.

Table 2 presents the accuracy rates for the given

dataset. Adaboost and random forest were performed

as classifiers to collect results by two types of tests:

5-fold and 10-fold cross validation. In an n-fold

cross validation test, the dataset is divided into n

subsets. One of these subsets is used for testing and

remaining n-1 subsets are used for training. This

procedure is repeated for n times.

1980 bins takes much computation time in both

random forest (2.72 seconds) and adaboost (18.4

seconds) algorithms. Whereas using 100 bins takes

2.26 and 1.64 seconds in a 10-fold cross validation

tests. That’s why, we decreased the number of bins

used for building the classifier. Table 1 shows the

time spent for training for 5, 10, 100, 200 and 1980

bins with random forest and adaboost algorithms.

In order to find out the most appropriate number

of bins to select, the procedure is as follows: we

have extracted the histogram of each image in the

class. The average of each bin is calculated over 700

samples for both classes. Afterwards, absolute

difference between two classes is calculated. A

predefined number of bins with highest difference

value were chosen to represent the class for training

and testing steps.

Table 1: Time for training with different methods.

Method Test 5 10 100 200 1980

Random

Forest

5-fold 1.25 1.45 2.23 2.64 2.69

10- fold 1.36 1.52 2.26 2.65 2.72

Adaboost

5-fold 0.11 0.19 1.33 2.64 18.4

10- fold 0.13 0.16 1.64 2.48 19.4

Table 2 illustrates the recognition rates for five

different sets. The first four sets consist of the first 5,

10, 100 and 200 highest value bins of the inter-class

difference vector respectively. The last set includes

the full length vector with 1980 bins.

Table 2: Accuracy rates for gender recognition.

Method Test 5 10 100 200 1980

Random

Forest

5-fold 78.1 84.9 89.5 90.4 92.3

10- fold 78 84.2 89.4 90.6 92.5

Adaboost

5-fold 75.6 76.8 81.7 82.1 85.6

10- fold 75.1 76 81.3 81.5 84.7

For all cases, random forest reveals more

accurate recognition than adaboost. Adaboost can

reach its highest accuracy rate of 85.6% when it is

using 1980 bins and it is consuming 18.4 seconds.

Using 1980 bins gives the highest recognition rate

but requires excess time for building the classifiers.

Optimum results are obtained in terms of

computation time and accuracy rate with use of 100

bins for both methods. Using 200 bins results in

lower accuracy rate when compared to 1980 bins

test. However, it consumes almost same time for

training. In contrast, 5-bins set takes the smallest

computation time but gives lowest accuracy.

For the appointed purpose, the optimum

selection is 100 bins. It gives a high accuracy rate of

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

110

89.5% for random forest and 81.7% for adaboost at a

comparatively low processing time when compared

to 200 bins and 1980 bins versions.

5 CONCLUSION

In this paper, a new study is given for gender

recognition problem in public areas where facial

features can`t be extracted. Inter-class difference

vector is maximized and selected number of bins

with highest value of this vector are used to build the

classifiers with both random forest and adaboost

algorithms. Five different sets are used and for our

purpose 100 bins set gives the most satisfying

results. It gives us 89.5% and 81.7% recognition

rates random forest and adaboost respectively.

As a further study, we will apply the same

method as multi-feature model with adding colour

information or modified features.

ACKNOWLEDGEMENTS

This research was supported by Basic Science

Research Program through the National Foundation

of Korea (NRF) funded by the Ministry of Science,

ICT and Future Planning (2012M3C1A1048865)

and Busan Brain 21 funded by Busan City.

REFERENCES

L. A. Alexandre, 2010. Gender recognition: A multiscale

decision fusion approach, Pattern Recognition Letters,

Vol.31,No.11, pp.1422–1427.

J. Wu, W. Smith, and E. Hancock, 2011. Gender

discriminating models from facial surface normals,

PR, Vol.44, No. 12, pp.2871–2886.

J. Wu, W. Smith, and E. Hancock, 2010. Facial gender

classification using shape-from-shading, IVC, Vol. 28,

No. 6, pp.1039–1048.

E. Makinen and R. Raisamo,2008. Evaluation of gender

classification methods with automatically detected and

aligned faces, PAMI, Vol.30,No.3,pp.541–547.

F. S. Khan, J. van de Weijer, R. M. Anwer, M. Felsberg,

and C. Gatta, 2014. Semantic pyramids for gender and

action recognition, IEEE Trans. Image Process. ,

Vol.23, No.8, pp.3633–3645.

N. Zhang, R. Farrell, F. Iandola, and T. Darrell, 2013.

Deformable part descriptors for fine-grained

recognition and attribute prediction, Proc. IEEE Int.

Conf. Comput. Vis. pp.729–736.

C. B. Ng, Y. H. Tay, and B.-M. Goi, 2013. A

convolutional neural network for pedestrian gender

recognition, in Int. Symposium on Neural Networks

(ISNN’13), LNCS, Vol. 7951.

L. D. Bourdev, S. Maji, and J. Malik, 2011. Describing

people: A poselet-based approach to attribute

classification, in IEEE Int. Conf. on Comp. Vis.

(ICCV).

J. Gou, L.Gao, P. Hou, and C. Hu, 2012. Gender

recognition based on multiple scale textural feature,

5th Int. Congress on Image and Signal Process.,

Sichuan, China.

S. M. E. Hossain and G. Chetty, 2011. Next generation

biometric identity verification based on face- gait

biometrics, Int. Conf. on Biomedical Eng. and Tech. ,

Kuala Lumpur, Malaysia.

Mustafa E. Yildirim, J. S. Park, J. Song, and B. W. Yoon,

2014. Gender Classification Based on Binary Haar

Cascade, International Journal of Computer and

Comm. Eng. , Vol. 3, No.2.

C. B. Ng, Y. H. Tay, and B. M. Goi, 2012. Vision-based

human gender recognition: A survey, in Proc. Pacific

Rim Int. Conf. Artif. Intell.: Trends Artif. Intell., pp.

335–346.

N. Dalal and B. Triggs, 2005. Histograms of oriented

gradients for human detection, Proc. of the IEEE

Computer Society Conf. on Comp. Vis. and Patt. Rec.,

Vol.1, pp.886-893.

A. Bosch, A. Zisserman, 2007. Representing shape with

spatial pyramid kernel, in Proc. of the 6

ACM Int.

Conf. on Image and video retriveal. pp. 401-408.

S. Lazebnik, C. Schmid, and J. Ponce, 2006. Beyond Bags

of Features: Spatial Pyramid Matching for

Recognizing Natural Scene Categories, in IEEE Comp.

Society, Conf. on Comp. Vis. and PR. Vol. 2, pp.

2169-2178.

A. Bosch, A. Zisserman, and X. Munoz, 2007. Image

Classification Using Random Forests and Ferns, Proc.

11th IEEE Int. Conf. Comp. Vis. Conf.

M. Collins, J. Zhang, and P. Miller, 2009. Full body image

feature representations for gender profiling, in IEEE

Int. Conf. On Comp. Vis. Workshops, pp.1235-

1242.

Y. Freund and R. Schapire, 1996. Experiments with a new

boosting algorithm, Int. Conf. Machine Learning.

Gender Recognition using Hog with Maximized Inter-Class Difference

111