Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks
Journal article, Peer reviewed
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Original versionMohan, Seal, Krejcar, Yazidi. Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks. IEEE Transactions on Instrumentation and Measurement. 2020 https://doi.org/10.1109/TIM.2020.3031835
An image is worth a thousand words; hence, a face image illustrates extensive details about the speciﬁcation, gender, age, and emotional states of mind. Facial expressions play an important role in community-based interactions and are often used in the behavioral analysis of emotions. Recognition of automatic facial expressions from a facial image is a challenging task in the computer vision community and admits a large set of applications, such as driver safety, human–computer interactions, health care, behavioral science, video conferencing, cognitive science, and others. In this work, a deep-learning-based scheme is proposed for identifying the facial expression of a person. The proposed method consists of two parts. The former one ﬁnds out local features from face images using a local gravitational force descriptor, while, in the latter part, the descriptor is fed into a novel deep convolution neural network (DCNN) model. The proposed DCNN has two branches. The ﬁrst branch explores geo-metric features, such as edges, curves, and lines, whereas holistic features are extracted by the second branch. Finally, the score-level fusion technique is adopted to compute the ﬁnal classiﬁca-tion score. The proposed method along with 25 state-of-the-art methods is implemented on ﬁve benchmark available databases, namely, Facial Expression Recognition 2013, Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, and Real-world Affective Faces. The data-bases consist of seven basic emotions: neutral, happiness, anger, sadness, fear, disgust, and surprise. The proposed method is compared with existing approaches using four evaluation metrics, namely, accuracy, precision, recall, and f1-score. The obtained results demonstrate that the proposed method outperforms all state-of-the-art methods on all the databases.