5,848 research outputs found

    Video surveillance for monitoring driver's fatigue and distraction

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    Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers

    Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

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    Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

    Selective Refinement Network for High Performance Face Detection

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    High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author: Shifeng Zhang ([email protected]
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