We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.

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The harmful effects of cell phone usage on driver behavior have been well investigated and the growing problem has motivated several several research efforts aimed at developing automated cell phone usage detection systems. Computer vision based approaches for dealing with this problem have only emerged in recent years. In this paper, we present a vision based method to automatically determine if a driver is holding a cell phone close to one of his/her ears (thus keeping only one hand on the steering wheel) and quantitatively demonstrate the method's efficacy on challenging Strategic Highway Research Program (SHRP2) face view videos from the head pose validation data that was acquired to monitor driver head pose variation under naturalistic driving conditions. To the best of our knowledge, this is the first such evaluation carried out using this relatively new data. Our approach utilizes the Supervised Descent Method (SDM) based facial landmark tracking algorithm to track the locations of facial landmarks in order to extract a crop of the region of interest. Following this, features are extracted from the crop and are classified using previously trained classifiers in order to determine if a driver is holding a cell phone. We adopt a through approach and benchmark the performance obtained using raw pixels and Histogram of Oriented Gradients (HOG) features in combination with various classifiers.

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