The nature of statistical learning theory
The nature of statistical learning theory
Synthesis of Novel Views from a Single Face Image
International Journal of Computer Vision
Learning and example selection for object and pattern detection
Learning and example selection for object and pattern detection
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically Motivated Face Selective Attention Model
Neural Information Processing
Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Spatially-coherent pyramid matching based on max-pooling
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Biologically motivated visual selective attention for face localization
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Texture analysis and classification: A complex network-based approach
Information Sciences: an International Journal
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Models of object recognition in cortex have so far been mostly applied to tasks involving the recognition of isolated objects presented on blank backgrounds. However, ultimately models of the visual system have to prove themselves in real world object recognition tasks. Here we took a first step in this direction: We investigated the performance of the HMAX model of object recognition in cortex recently presented by Riesenhuber & Poggio [1,2] on the task of face detection using natural images. We found that the standard version of HMAX performs rather poorly on this task, due to the low specificity of the hardwired feature set of C2 units in the model (corresponding to neurons in intermediate visual area V4) that do not show any particular tuning for faces vs. background. We show how visual features of intermediate complexity can be learned in HMAX using a simple learning rule. Using this rule, HMAX outperforms a classical machine vision face detection system presented in the literature. This suggests an important role for the set of features in intermediate visual areas in object recognition.