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Neural Network-Based Face Detection

Neural Network-Based Face Detection,10.1109/34.655647,IEEE Transactions on Pattern Analysis and Machine Intelligence,Henry A. Rowley,Shumeet Baluja,Ta

Neural Network-Based Face Detection   (Citations: 1734)
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We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The systemarbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images,can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented; showing that our system has comparable performance in terms of detection and false-positive rates.
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    • ...Many methods have been recently proposed to automatically detect abandoned objects (parked vehicles and left-luggage) in video surveillance [1], [3]–[5], [7], [9], [13]–[15], [19], [20], [24], [25], [28]–[32], [36]–[38], [44] for different applications, such as traffic monitoring, public safety, retail, etc...
    • ...A cascade classifier was trained by considering 4000 faces and 4000 nonfaces at each level, where the nonface samples were obtained through bootstrap [28]...

    YingLi Tianet al. Robust Detection of Abandoned and Removed Objects in Complex Surveilla...

    • ...Learning-based detection mainly studies ways to detect the face, such as when Rowley learned about a lot of different facial features by using the neural network [6, 7]. The learning process with the neural network compares the whole image to find the location of the face...

    Ying-Wen Baiet al. Using image processing methods to reduce dazzle in the eyes from a dig...

    • ...This was followed by other pattern recognition approaches including linear discriminant techniques [15], neural network based algorithms [16] and methods based on graphical models [17]...

    Pavan Turagaet al. Diamond Sentry: Integrating Sensors and Cameras for Real-Time Monitori...

    • ...Furthermore, the classification process can always be made into a two-stage process, where a simple linear classifier eliminates trivial background patches quickly in the initial stage, as in [46], [58]...

    Quan Yuanet al. Learning a Family of Detectors via Multiplicative Kernels

    • ...Since these early efforts, more powerful techniques have emerged as evidenced by the considerable success of machine learning applied to human face detection (Rowley et al. 1998; Osuna et al. 1997; Viola and Jones 2001)...
    • ...Feed-forward neural networks have been used successfully in a number of visual pattern recognition applications (Rowley et al. 1998; 346 Mach Learn (2011) 84:341–367...

    Michael C. Burlet al. Onboard object recognition for planetary exploration

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