科研之友
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摘要
Gait-based gender identification has received great attention from biometric researchers in the vision field because of its potential in different applications. Gait-based gender identification will help a human identification system to focus only on the identified gender-related features, which can improve the search speed and efficiency of the retrieval system by limiting the subsequent searching space to either a male database or a female database. In this study, after preprocessing, five binary moment features and four spatial features are extracted from a human silhouette. Then the extracted features are used for training and testing pattern classifiers. We have successfully achieved our objective with one gait cycle and nine features of normal video sequences only. To evaluate the performance of the proposed algorithm, experiments have been conducted by using probablistic neural network (PNN) and support vector machine (SVM) on the benchmark CASIA B database. Experimental results show superior performance of our approach in terms of correct classification rate, and it shows robustness to variations in clothing and carrying condition.
- 出版日期2013
