Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Face Authentication Using Morphological Dynamic Link Architecture
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Angle Determination by Face, Eyes and Nose Localization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Face Detection with High Precision Based on Radial-Symmetry Transform and Eye-Pair Checking
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Journal of Cognitive Neuroscience
IEEE Transactions on Image Processing
Face authentication with Gabor information on deformable graphs
IEEE Transactions on Image Processing
An improved algorithm for face recognition using wavelet and facial parameters
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
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A novel algorithm for extracting the regions of interest (ROI) from face images is presented in this paper. The novelty of the algorithm comes from its multi-resolution cellular analysis coupled with an adaptive thresholding technique incorporating a unique idea of exponential averaging. The complexity of the cellular ROIs reported by the algorithm from the frontal face view as input, is further controllable by the chosen cell size, which is its added advantage. Apart from the actual ROIs representing the eye pair, nostrils, and the mouth area, some regions of non-interest may also creep in while extracting the set of cellular regions from the face image, which are discarded by a simple geometric analysis using a containment tree. The containment tree, which is newly introduced in this paper, captures the underlying relationship of the cellular regions, which, when analyzed, returns the face ROIs in an elegant representation. Since the entire algorithm works purely in the integer domain with primitive operations (comparison, right shift, and addition) only, it runs very fast for both gray-scale and color images. Some experimental results on different facial images demonstrate its speed, robustness, and efficiency.