Fast facial expression recognition using Boosted Histogram of Oriented Gradient (BHOG) features

Systems for automatic facial expression recognition (FER) have an enormous need in advanced human-computer interaction (HCI) and human-robot interaction (HRI) applications. Over the years, researchers developed many handcrafted feature descriptors for the FER task. These descriptors delivered good accuracy on publicly available FER benchmark datasets. However, these descriptors generate high dimensional features that increase the computational time of the classifiers. Also, a significant proportion of the features are irrelevant and do not provide additional information for facial expression analysis. Adversely, these redundant features degrade the classification accuracy of the FER algorithm. This study presents an alternate, simple, and efficient scheme for FER in static images using the Boosted Histogram of Oriented Gradient (BHOG) descriptor. The proposed BHOG descriptor employs the AdaBoost feature selection algorithm to select important facial features from the original high-dimensional Histogram of Oriented Gradient (HOG) features. The BHOG descriptor with a reduced feature dimension decreases the computational cost without diminishing the recognition accuracy. The proposed FER pipeline tuned on the optimal values of different hyperparameters achieves competitive recognition accuracy on five benchmark FER datasets, namely CK+, JAFFE, RaFD, TFE, and RAF-DB. Also, the cross-dataset experiments confirm the superior generalization performance of the proposed FER pipeline. Finally, the comparative analysis results with existing FER techniques revealed the effectiveness of the pipeline. The proposed FER scheme is computationally efficient and classifies facial expressions in real time.