Facial expression recognition using digital signature feature descriptor

Facial feature extraction is the most crucial part for efficient representation of facial images. For facial expression recognition, efficiency of recognition mainly depends on discriminative nature of features that describe optical changes of facial expressions. Facial expressions are very dynamic in nature. The dynamics of facial muscle changes is required to be captured and encoded accurately. Facial expressions are contractions and expansions of facial muscles which results in high-frequency edges on different part of facial image. A novel feature extraction framework called digital signature based on high-frequency edges in combination with LBP histogram features is developed, and the proposed methodology is used for facial expression recognition (FER). Digital signature descriptor of facial dynamic is obtained by projecting edge pixels vertically and horizontally. Digital signature uniquely and completely describes the facial expressions. Support vector method classifier is used to classify six basic expressions based on one-against-all strategy of classification. The validity of proposed algorithm is tested on standard widely used Cohn–Kanade Facial Expression Database (CKFED), Taiwanese Facial Expression Database (TFED) and Japanese Female Facial Expression databases (JFED). Experimental results show that the efficiency of expression recognition of proposed novel technique is 96.25% on CKFED which is higher than other existing methods.