Accurate facial feature localization on expressional face images based on a graphical model approach
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Viola-Jones based detectors: how much affects the training set?
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A geometric approach to face detector combining
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
MethMorph: simulating facial deformation due to methamphatamine usage
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
An study on ear detection and its applications to face detection
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
Dataset for the evaluation of eye detector for gaze estimation
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Adaptive Haar-like classifier for eye status detection under non-ideal lighting conditions
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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The human face provides useful information during interaction; therefore, any system integrating Vision-Based Human Computer Interaction requires fast and reliable face and facial feature detection. Different approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola–Jones object detection framework that particularly in the context of facial processing has been frequently used. The OpenCV community shares a collection of public domain classifiers for the face detection scenario. However, these classifiers have been trained in different conditions and with different data but rarely tested on the same datasets. In this paper, we try to fill that gap by analyzing the individual performance of all those public classifiers presenting their pros and cons with the aim of defining a baseline for other approaches. Solid comparisons will also help researchers to choose a specific classifier for their particular scenario. The experimental setup also describes some heuristics to increase the facial feature detection rate while reducing the face false detection rate.