Facial Eigen-Feature based gender recognition with an improved genetic algorithm

• Published in: Journal of intelligent & fuzzy systems Vol. 37; no. 4; pp. 4891 - 4902
• Main Authors: Zhou, Yimin, Li, Zhifei
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2019; Sage Publications Ltd
• Subjects: Classification; Classifiers; Datasets; Face recognition; Feature extraction; Feature recognition; Gender; Genetic algorithms; Identification methods; Image processing; Neural networks; Object recognition; Principal components analysis; Gender recognition; Genetic feature selection; Neural network; Frontal face; Principal component analysis
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-17193

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.