| dc.description.abstract | Recently, gender classification is widely developed in many commercial systems in our daily life. For examples, the statistical data collection module for consumers’ genders and ages is embedded into an advertisement system. An intelligent surveillance algorithm analyzes the human gender and activities. Many gender classification algorithms proposed in literatures use the face, voice or gait features. However, face and voice features need a close contact with people. Human gaits are the valid feature for gender classification in a long distance. The main challenge for gait-based gender classification is the view angle from cameras due to the non-rigid body. In addition to view angles, clothing, shoes, and carrying conditions also reduce the performance.
In this work, a gait energy image (GEI)-based algorithm is proposed for gender classification. The GEIs are constructed from the aligned gait silhouettes. The gait cycles are first estimated and the silhouettes of gaits are aligned from the input video sequences. After the pre-processing, gait cycle estimation, and silhouette alignment, GEIs are constructed and separated into several regions. Next, local texture features, local binary pattern (LBP) or local directional pattern (LDP), are extracted from the separated GEIs’ regions. A feature vector concatenating the LBP histograms of small regions are extracted. An SVM-based classifier is adopted for gender classification.
In order to show the performance of the proposed method, several conditions, including various view angles, clothing variations, and carrying bags, are conducted in the experiments. Instead of the training and testing images both in a 90-degree angle, test images in various view angles are classified using the training images in a 90-degree angle. Experimental results are demonstrated and high recognition rates are achieved.
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