In defense of low-level structural features and SVMs for facial attribute classification: Application to detection of eye state, Mouth State, and eyeglasses in the wild

The current trend in image analysis is to employ automatically detected feature types, such as those obtained using deep-learning techniques. For some applications, however, manually crafted features such as Histogram of Oriented Gradients (HOG) continue to yield better performance in demanding situations. This paper considers both approaches for the problem of facial attribute classification, for images obtained “in the wild.” Attributes of particular interest are eye state (open/closed), mouth state (open/closed), and eyeglasses (present/absent). We present a full face-processing pipeline that employs conventional machine learning techniques, from detection to attribute classification. Experimental results have indicated better performance using RootSIFT with a conventional support-vector machine (SVM) approach, as compared to deep-learning approaches that have been reported in the literature. Our proposed open/closed eye classifier has yielded an accuracy of 99.3% on the CEW dataset, and an accuracy of 98.7% on the ZJU dataset. Similarly, our proposed open/closed mouth classifier has achieved performance similar to deep learning. Also, our proposed presence/absence eyeglasses classifier delivered very good performance, being the best method on LFWA, and second best for the CelebA dataset. The system reported here runs at 30 fps on HD-sized video using a CPU-only implementation.