Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection Using Mixture of MLP Experts
Neural Processing Letters
Journal of Cognitive Neuroscience
Adaptive mixtures of local experts
Neural Computation
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper introduces a novel, effective applicability of features inspired by visual ventral stream and biologically-motivated classification model, mixture of experts network for face/nonface recognition task. It describes a feature extracting system that derives from a feedforward model of visual cortex and builds a set of pose, facial expression, illumination and view invariant C1 features from all images in the dataset. Also, mixture of MLP experts network is a classifier which demonstrates high generalization capabilities in many different tasks. In accordance to these biological evidences, we propose face/nonface recognition model which combine these two techniques for the robust face/nonface problem. Experimental results using the combination C1 features and mixture of MLP experts network classifier, obtains higher recognition rate than related works in face/nonface identification. In addition, experimental results demonstrate this method is illumination and view-invariant.