Most face applications depend heavily on the accuracy of the face and facial landmarks detectors employed. Prediction of attributes such as gender, age, and identity usually completely fail when the faces are badly aligned due to inaccurate facial landmark detection. Despite the impressive recent advances in face and facial landmark detection, little study is on the recovery from and detection of failures or inaccurate predictions. In this work we study two top recent facial landmark detectors and devise confidence models for their outputs. We validate our failure detection approaches on standard benchmarks (AFLW, HELEN) and correctly identify more than 40 of the failures in the outputs of the landmark detectors. Moreover, with our failure detection we can achieve a 12 error reduction on a gender estimation application at the cost of a small increase in computation.
Usually, in face detection and facial landmark literature the reduction of failures is the direct result of trading off the time complexity running time of the methods. More complex models and intensive computations might allow for more robust performance. However, the costs for such reductions within the original models can be prohibitive for practical applications. For example, one might use a set of specialized detectors (or components) deployed together instead of a generic detector, and while the performance potentially can be improved, the time and memory complexities varies with the cardinality of the set @cite_10 @cite_12 @cite_4 .
@cite_4 @cite_10 @cite_12
2047508432 1849007038
We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new “in the wild” annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com). Face detection is a mature problem in computer vision. While diverse high performing face detectors have been proposed in the past, we present two surprising new top performance results. First, we show that a properly trained vanilla DPM reaches top performance, improving over commercial and research systems. Second, we show that a detector based on rigid templates - similar in structure to the Viola&Jones detector - can reach similar top performance on this task. Importantly, we discuss issues with existing evaluation benchmark and propose an improved procedure.