Interpretation of Occluded Face Detection Using Convolutional Neural Network

With the rapid development of artificial intelligence in past decades, great attention has been drawn to the field of face detection and recognition. Humans show a high degree of variability in their expressions, poses and appearance. Thus, limitations such as disguised and occluded faces, make it hard to implement high-accuracy face detection in real life. Although several algorithms have been proposed to handle recognition of disguised faces, the interpretation of the possible features that may have impact on the performance of models is hardly mentioned. In this paper, we explored possible features that could distinguish disguised faces compared to original faces, including skin region, luminosity, textures and edges. A Deep Neural Network model was utilised for the comparison between different features using the Disguised Faces in the Wild dataset. Our results show that colour on skin region, luminosity and texture in images could greatly contribute to the performance in detection of disguised faces with a CNN architecture. The results from fusing the individual features significantly outperformed the results when using the whole image, performing 72%, which is considered the state-of-the-art in subject-independent disguised face detection.