Detecting faces in images: a survey Export

@article{citeulike:614159, 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 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}, address = {Los Alamitos, CA, USA}, author = {Yang, Ming-Hsuan and Kriegman, D. J. and Ahuja, N.}, booktitle = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, citeulike-article-id = {614159}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/34.982883}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/34.982883}, citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=982883}, day = {07}, doi = {10.1109/34.982883}, issn = {0162-8828}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, keywords = {computer\_vision, machine\_learning, survey}, month = {August}, number = {1}, pages = {34--58}, posted-at = {2007-06-19 17:01:58}, priority = {2}, publisher = {IEEE Computer Society}, title = {Detecting faces in images: a survey}, url = {http://dx.doi.org/10.1109/34.982883}, volume = {24}, year = {2002} }

TY - JOUR ID - citeulike:614159 L3 - citeulike-article-id:614159 N2 - 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 IS - 1 JF - IEEE Transactions on Pattern Analysis and Machine Intelligence SN - 0162-8828 AD - Los Alamitos, CA, USA EP - 58 TI - Detecting faces in images: a survey PB - IEEE Computer Society VL - 24 T2 - Pattern Analysis and Machine Intelligence, IEEE Transactions on SP - 34 KW - computer_vision KW - machine_learning KW - survey AU - Yang, Ming-Hsuan AU - Kriegman, DJ AU - Ahuja, N PY - 2002/08/07/ UR - http://dx.doi.org/10.1109/34.982883 ER -