Classifier for Face Recognition Based on Deep Convolutional - Optimized Kernel Extreme Learning Machine

Face recognition task has been an active research area in recent years in computer vision and biometrics. Feature extraction and classification are the most significant steps for accurate face recognition systems. Conventionally, the Eigenface approach or frequency domain features have been used for feature extraction, but they are not invariant to outdoor conditions like lighting, pose, expression, and occlusion. Multiple convolutional and pooling layers of Deep Learning Networks (DLN) will efficiently extract the face database’s high-level features in the present work. These features have given to the Kernel Extreme Learning Machine (KELM) classifier, whose parameters have optimized using Particle Swarm Optimization (PSO). The proposed Deep Convolutional-Optimized Kernel Extreme Learning Machine (DC-OKELM) algorithm leads to better performance results and fast learning speed than classification using deep neural networks. The performance of DC-OKELM has evaluated on four standards face databases: AT&T, CMU-PIE, Yale Faces, and UMIST. Experimental results have compared with other state-of-the-art classifiers in terms of error rate and network training time, which shows the proposed DC-OKELM classifier’s effectiveness.