Affiliations: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
Correspondence:
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Corresponding author. Yimin Zhou, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing 518055, China. E-mails: [email protected] and [email protected].
Abstract: This paper proposes a novel image processing method to extract the gender feature from frontal face combining Principal Component Analysis (PCA) and an improved Genetic Algorithm (GA) to reduce the interference of facial expression, lighting or wear. The collected facial images are first cropped and aligned automatically, then the gray-level information can be converted to feature vectors via PCA. After eigen-features are extracted with high classification performance by the aid of an improved GA, the neural network classifier can be trained accordingly. Compared to the classification methods based on global gray-level information, the obtained classifier has better identification rate but less used feature dimension, so the calculation load can substantially be reduced during training and classification procedures, which benefits to the development of a real-time identification system. Furthermore, FERET dataset and FEI dataset are used to validate the generality of the proposed method, where 94% and 96% accuracy rates of the gender recognition can be achieved respectively.
Keywords: Gender recognition, Genetic feature selection, Neural network, Principal component analysis, Frontal face
