Enhancing the Performance of Active Shape Models in Face Recognition Applications

Biometric features in face recognition systems are one of the most reliable and least intrusive alternatives for personal identity authentication. Active shape model (ASM) is an adaptive shape matching technique that has been used often for locating facial features in face images. However, the performance of ASM can degrade substantially in the presence of noise or near the face frame contours. In this correspondence, we propose a new ASM landmark selection scheme to improve the ASM performance in face recognition applications. The proposed scheme selects robust landmark points where relevant facial features are found and assigns higher weights to their corresponding features in the face classification stage. The experimental results are promising and indicate that our approach tends to enhance the performance of ASM, leading to improvements in the final face classification results.