Local double directional stride maximum patterns for facial expression retrieval. (11th July 2022) - Maheswari, V. Uma; Varaprasad, G.; Viswanadharaju, S.
Face recognition and expression recognition are playing vital roles in various applications such as medical field, entertainment, criminal analysis, social media, online business, etc. Local texture feature descriptors such as LBP, LTrP, LTP, and DBC are usually popular to recognise the faces and expressions as well. In this paper, a new feature descriptor local double directional stride maximum pattern approach is proposed as existing methods are suffering from intensity differences and covering of diagonal information. The proposed approach will identify the facial expression based on the directional coordination of pixels, the pattern will be generated by calculating the first order derivatives in four directions using DBC, and then second order derivatives are calculated maximum and minimum intensity values in four directions among three pixels in every direction to construct the feature. This approach reaches to recognise the major intensity differences by using the directions and pleats the accurate data from an image. Facial expression recognition and retrieval performance is measured and compared in stand of precision, recall and ARR on the benchmark datasets such as JAFFE, CK+, ISED, RaFD, etc. with the existing methods.