Facial expression, as one of the most significant means for human beings to show their emotions and intensions in the process of communication, plays a significant role in human interfaces. In recent years, facial expression recognition has been under especially intensive investigation, due conceivably to its vital applications in various fields including virtual reality, intelligent tutoring system, health-care and data driven animation. The main target of facial expression recognition is to identify the human emotional state (e.g., anger, contempt, disgust, fear, happiness, sadness, and surprise ) based on the given facial images. This paper deals with the Facial expression detection and recognition through Viola-jones algorithm and HCNN using LSTM method. It improves the hypothesis execution enough and meanwhile inconceivably reduces the computational costs. In feature matching, the author proposes Hybrid Scale-Invariant Feature Transform (SIFT) with double δ-LBP (Dδ-LBP) and it utilizes the fixed facial landmark localization approach and SIFT’s orientation assignment, to obtain the features that are illumination and pose independent. For face detection, basically we utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the Hybrid Convolutional Neural Network (HCNN) and Long Short-Term Memory (LSTM) model for identifying the facial expression in efficient manner.The experimental result confirms that the HCNN-LSTM Model beats traditional deep-learning and machine-learning techniques with respect to precision, recall, f-measure, and accuracy using CK+ database. Proposes Hybrid Scale-Invariant Feature Transform (SIFT) with double δ-LBP (Dδ-LBP) and it utilizes the fixed facial landmark localization approach and SIFT’s orientation assignment, to obtain the features that are illumination and pose independent. And HCNN and LSTM model for identifying the facial expression.
Facial Landmark Localization in Depth Images Using Supervised Ridge Descent
