Detecting Faces in Images: A Survey

By Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

4,083 citations recorded by the source corpus.

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 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid 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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ebb34b75982f628f9ce5995821fff81fd967dc2d

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Citations in the corpus

Human computing and machine understanding of human behavior: a survey (2006, background): “, identification of all regions in the scene that contain a human face [89], [44].”
Localizing Parts of Faces Using a Consensus of Exemplars (2013, background): “OVER the last decade, new applications in computer vision and computational photography have arisen due to earlier advances in methods for detecting human faces in images [27], [29].”
Recent advances in visual and infrared face recognition - a review (2005, background): “Recent survey papers on face detection techniques can be found in [35,36].”
Personal Verification Using Palmprint and Hand Geometry Biometric (2003, background): “The reliability of personal identification using face is currently low as the researchers today continue to grapple with the problems of pose, lighting, orientation and gesture [2].”
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing (2012, neutral): “This is the second step of the process and it involves technologies like face detection [80], speaker segmentation [81], tracking [82], etc.”
Synergistic Face Detection and Pose Estimation with Energy-Based Models (2004, neutral): “The resulting system is very slow.”
Appearance-based eye gaze estimation (2002, methodology): “Algorithms using this representation are often referred to as being appearance-based or view-based, and they have been successfully applied to object recognition and detection [2, 8, 12, 15].”
A discriminative feature space for detecting and recognizing faces (2004, background): “For face detection, the normalized pixel values [6] [20] and Haar-like features [18] are the most considered ones.”
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection (2019, background): “, segmentation [39], image recognition [38, 30]), or even in the modern tasks such as image-to-image translation [41], photo sketching [18] and so on.”
A survey of skin-color modeling and detection methods (2007, background): “Skin color can also be used as complimentary information to other features such as shape and geometry and can be used to build accurate face detection systems [1] [2] [3] [4].”
Facial action recognition for facial expression analysis from static face images (2004, background): “Possible strategies for face detection vary a lot, depending on the type of input images [31].”
Modeling naturalistic affective states via facial and vocal expressions recognition (2006, background): “The goal of face detection is to determine whether or not there are faces in the image, and if yes, return the image location and extent of each face [35].”
Face localization and tracking in the neural abstraction pyramid (2005, neutral): “, by Hjelmas and Kee Low [7] and by Yang, Kriegman, and Ahuja [17].”