A parameter-based combined classifier for invariant facial expression and gender recognition

In this paper an learning vector quantization network architecture based on varying parameters and eliminating blind neurons²⁸ is developed that learns the correlation of gender patterns and recognizes facial expressions of human faces. The network is developed to classify the 1 - neutral, 2 - smile or happiness, 3 - anger and 4 - scream or fear expressions. A peak accuracy rate of (a) 100% on the expression recognition task and (b) 100% on the gender recognition task for random training and test samples is achieved. The computer execution time for the recognition is about 0.004 seconds per face image for the latex expression classification task and 0.02 for the gender recognition task on an IBM PC. It is demonstrated that the proposed architecture is capable of achieving better recognition results with both good generalization performance and a fast training-time on a variety of test problems. The developed system showed potential for real life application domains.