Multi-stage binary patterns for facial expression recognition in real world

Facial expression recognition in real world has always been a challenging task in computer vision. Expressions are spawned from highly bendable features and vary in cultures, genders and ages among individuals. Moreover, complex backgrounds, intricate settings and varied illumination conditions add to complexity of recognition. Local binary patterns (LBP) capture the local region properties from images so these are extensively used for facial expression recognition. But still LBP and its variations including local gradient coding horizontal diagonal direction (LGC-HD), local gradient coding horizontal vertical diagonal direction (LGC-HVD) and compound local binary pattern (CLBP) are not able to resolve the issues of local illuminations that might result from disproportionate light, certain obscurities, shadows or use of accessories on the face. In proposed technique, multi-stage binary code is generated for each comparison against neighbouring pixel. LBP records only sign difference which eventually discards some significant texture information. InMSBPsign difference along with gradient difference accumulates changes along edges such as area around eye brows, eyes, mouth, bulges and wrinkles. MSBPis then compared withLBP, LGC-HD, LGC-HVDand CLBP. LGC-HD, LGCHVD and CLBP centered on experimentation using two approaches. These techniques are tested on dataset containing full face image named holistic and then division based approach. Experiments are performed using static facial expression in wild dataset. Results show that proposed approachMSBPoutperforms other techniques with 96% accuracy in holistic approach and 60% accuracy in division based approach. Further, it is also ascertained from the experiments that all these approaches work better in holistic approach than division based approach.