Comparative Study on Hidden Markov Model Versus Support Vector Machine: A Component-Based Method for Better Face Recognition

In this paper, we report a comprehensive study of two well evolved and developed learning algorithms for effective Face Recognition (FR); viz. the Hidden Markov Model (HMM) and Support Vector Machines (SVM). It is evident that, the accuracy of recognition and efficiency in terms of time and speed of a FR system are directly proportional to the competency of the underlying learning algorithms. Here, we propose to compare the two acclaimed method stated above. A component-based approach is adapted to train face images for recognition. Each face image is divided into sub-states for both HMM and SVM algorithm. In attempt to achieve better rates of recognition, all face images are pre-processed and resizing which helps in reducing the overall complexity of the FR system. We run this proposed system against benchmarks accredited by previous researches.