Detecting faces in images: A survey

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Detecting faces in images: A survey (2002)

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by Ming-hsuan Yang , David J. Kriegman , Narendra Ahuja

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	838 - 4 self

BibTeX

@ARTICLE{Yang02detectingfaces,
    author = {Ming-hsuan Yang and David J. Kriegman and Narendra Ahuja},
    title = {Detecting faces in images: A survey},
    journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
    year = {2002},
    volume = {24},
    number = {1}
}

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Abstract

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

Keyphrases

single image lighting condition face tracking intelligent vision-based human computer interaction evaluation metric image region several promising direction high degree relevant issue pose estimation research effort face processing face image face regardless face recognition data collection three-dimensional position face detection expression recognition numerous technique efficient face detection algorithm future research image sequence

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