Gender Classification Using Scaled Conjugate Gradient Back Propagation Computer Science Department Gender Classification Using Scaled Conjugate Gradient Back Propagation Authors : Assist.Prof. Dr. Abbas H. Hassin Al-Asadi. : Entesar Barges Talal. Date : 25/2/2016.

Outlines Introduction The Proposed Algorithm Conclusion Suggestion for future works

Introduction Gait:-a particular way or manner of moving on foot. Gait cycle:  The gait cycle is the time interval between the exact same repetitive events of walking. The defined cycle can start at any moment, but it is generally begin when one foot contacts the ground. If it starts with the right foot contacting the ground, then the cycle ends when the right foot makes contact again.   There are two phases of the gait cycle: Stance phase. Swing phase. Gait recognition :The term gait recognition signifies the identification of an individual from a video sequence of the subject walking. This does not mean that gait is limited to walking, it can also be applied to running or any means of movement on foot.

Gender Classification   Gender classification :is to determine a person's gender, e.g., male or female, based on his or her biometric cues. In this correspondence, we present method of gender classification based on human gait.

The Proposed Algorithm Input Video Frames Eigenbackgrounds Preprocessing If mean image<=0.0400 Morphological Drawn Bounding Box

Compute Three Local Min Compute Aspect Ratio Gait Cycle Detection Compute Three Local Min Compute Gait Energy Image Feature Extraction Compute Tamura Texture Feature ?train Recognition No Yes NN Classification Data base Male/ Female

Preprocessing Input Video Frames Morphological Detection Eigenbackgrounds If mean image<=0.0400 Morphological Detection

Aspect Ratio =Height/Width (Of Bounding Box). Gait Cycle Detection Compute Aspect Ratio: Aspect Ratio =Height/Width (Of Bounding Box). Smooth The Aspect: Smooth the aspect ratio by using moving average filter using a 20-point sliding neighborhood. Detect The Index: Detect the index of the first minimum and the index of the third minimum, gait cycle represents the interval that detect between the two indexes.

Coarseness/contrast/directionality Feature Extraction Tamura Texture Feature The texture features are coarseness, contrast and directionality from tamura method that extract from gait energy image of one gait cycle. Gait energy image Coarseness/contrast/directionality Feature vector

Classification Using Neural Network Feature vector Data base 144*3 Cross validation Train set 120*3 Test set 24*3 Male/Female

Classification Result from NN Classification Accuracy % Feature type Data type Test Data Train Data 98 100 Tamura texture Features Male Female 98%

Neural Network Training Performance

Neural Network training confusion matrix

Conclusions Gender classification system depended on tamura texture feature is more effective because it depended on gait energy image. This image avoid synchronization difficulties and prevent noises from individual images. This image consider model free gait approach , the model free gait recognition methods or appearance based methods work directly on the gait sequences. They are not considered as a model for the human body to rebuild human walking steps. They have the advantage of low computational cost in compare with model-based approaches and tamura feature considered third ordered texture feature, this type of feature not depended on only pixel but it depended on pixel neighborhoods and this fact give tamura feature strongest to effect by noise for all these reason the system give more robust solution to gender classification.

Suggestions for Future Works The proposed system was working on only side view person walking; in future work suggest dealing with multi view person walking. suggest dealing with wearing coat and carrying bag CASIS B database and apply the proposed system on CASIS A data base and CASIS C data base. References

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