1. Face Image-Based Gender Recognition Using Complex-Valued Neural Network Instructor :Dr. Dong-Chul Kim Indrani Gorripati

2. Contents ■Introduction ■Complex value Neural networks ■System flow chart ■Image processing ■Feature extraction ■Research data ■Classification architecture model ■Experimental results ■Conclusion and future work

3. Introduction ■Human faces can exhibit quite obvious sexual dimorphism. ■ In gender recognition, face texture is more prominent than face outline for frontal view of the face ■This research investigates the implementation of complex-valued neural network as a classifier to recognize human gender which is based on face image. ■The experiment is also aimed to study the comparison between complex-valued and real-valued neural network. ■ Methods proposed in this paper include image processing, feature extraction, and classification.

4. Contd.. ■ Some fields such as demographic census, access control, and other face analysis area such as age recognition, ethnic recognition, and face recognition can benefit from automatic gender recognition system. ■ This study uses two kind of neural network model, namely real-valued neural network and complex-valued neural network. Real-valued neural network system will classify gender based on features extracted from images which processed through two image processing methods separately. ■ complex-valued neural network system will classify gender based on combined features from both methods.

5. Complex value Neural Network ■Complex-valued neural network is a neural network which consists of complex- valued input and/or complex-valued treshold and/or complex-valued activation function. ■The most important characteristic of complex-valued neural network is its ability to process complex-valued information. such as electromagnetism, light waves, quantum waves, and image processing based on adaptive rotation signal. ■Complex-valued back propagation (Complex-BP) are utilized in learning process on complex-valued neural network. Complex-BP is an algorithm that can be applied on multilayered feed-forward neural network which consists of complex-valued weights, tresholds, input signals, and output signals.

6. System flow chart

7. Image processing  Viola-Jones framework is used for object (face) detection which utilizes the combination of 3 techniques: integral image representation, AdaBoost classification method, and a method to dispose the background part of the image is used to detect face

8. Local binary pattern process ■This process uses local binary pattern operator to accentuate the texture of image and gradient filter to make the out line of the face more prominent. ■Local binary pattern (LBP) operator works as follows. Each pixel on the image becomes the central pixel of the ͵ 3x3 ͵ sub-image, and the rest is filled by its neighbors. Then, for each pixel on the sub-image (except the central pixel), tresholding process is conducted.

9. Gradient filter ■Gradient filter works as follows. After the color intensity of the image equalized by using histogram equalization, the gradiet value for each pixel is counted by formula below ■ f x,y is the pixel value at the coordinate (x,y). After that, masking process is conducted to dispose unnecessary background region. The result is shown as follows.

10. Feature extraction ■The features are extracted from the image by using histogram of oriented gradient (HOG) which count the gradient orientation appearance on partial, overlapping image ■Histogram of oriented gradient applied globally and locally.  On global HOG, The image which features extracted is a full face image.  on local HOG the image which features extracted are partial face image (eyes, nose, and mouth). ■ Both local and global features then combined, which resulted in a vector that describes face image. ■To obtain complex feature vector, the HOG vector from the first image processing method and the second image processing method

11. Principal component analysis(PCA) ■ The dimension of the resulted HOG vector is reduced by using principal component analysis (PCA). ■ PCA is a method to identify patterns on a data, and expressing that data such that the similarities and the differences between each datum are more prominent. ■ PCA is a powerful mechanism to analyze data, the patterns of the data have been found then that data compressed so that there will be not much loss on the data.

12. Research data ■Experiment conducted by using FERET face image database. The images in FERET face image database are taken from human face without restriction to age, race, and expression, but the images used in this experiment restricted to frontal face image with neutral expression. ■This study takes four hundred frontal face images from FERET database. ■Two hundred images are used for training process and two hundred for testing process. In which hundred are male face images and hundred are female face images.

13. Classification Architecture Model ■ The topologies for both real-valued and complex valued neural network are closely similar. The difference lies in the number of perceptron that the real-valued has more complex network and more neurons than the complex-valued. ■ Topology consists of 3 layers: Input layer Hidden layer Output layer

14. Contd.. The Real-valued neural network classification system consists of 50 input signals, 31 perceptrons, 1210 connection weights, and 31 tresholds. All of 31 perceptrons composed by 20 perceptrons on the input layer, 10 peceptrons on the hidden layer, and 1 perceptron on the output layer. Meanwhile, the Complex-valued neural network classification system consists of 50 input signals, 16 perceptrons, 555 connection weights, and 16 tresholds. All of 16 perceptrons composed by 10 perceptrons on the input layer, 5 peceptrons on the hidden layer, and 1 perceptron on the output layer.

15. EXPERIMENTAL RESULTS ■There are two measurement parameters to be analyzed, namely processing time of the training phase and accuracy rate in recognizing gender. ■This experiment also uses two ways in detecting or cropping face images. First way, the face images are cropped manually, while the other one is automatically detected by using Viola-Jones framework.

16. The table shows the time needed for each training scheme to reach convergence point.

17. This is is comparation graph of the time needed to reach convergence point between real-valued neural network system and complex-valued neural network system

18. ■The accuracy rate for each testing scheme is shown in the following table.

19. ■The following is the comparation graph of accuracy rate between real-valued neural network and complex-valued neural network in every testing schemes.

20. Conclusion And Future Work ■This research is mainly aimed to study the implementation of complex-valued neural network for face image-based gender recognition system. The investigation is done towards some measurement parameters and compared to real-valued neural network to see impact of using the methods. Then, this study is successfully done to show the experiment result and behavior in the method implementation. ■ Base on the accuracy level, the complex-valued neural network and the real-valued one are tend to have equal performance. It is slightly above 80% of accuracy rate and it can probably be better if the previous stages.With the accuracy rates stated before, the complex-valued can reach four times faster than the real-valued. ■To develop this research further, some studies are needed to use holomorphic activation function, more robust image processing method, and more robust feature extraction method. A real time implementation should be tried as well to see the actual influences of the methods used.

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