Real-time gender recognition based on eigen-features selection from facial images
Yimin Zhou, Zhifei Li · 2016
This paper proposed a novel image processing method combining Principal Component Analysis (PCA) and Genetic Algorithm (GA) to reduce the interference of facial expression, lighting or wear but extracting gender feature from frontal face. 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 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 half less used feature dimension, so the calculation load can substantially be reduced during training and identification 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 92% and 94% accuracy rates of the gender recognition can be achieved respectively.