Using biologically inspired features for face processing

Keyphrases

• Face Processing 100%
• Visual Features 100%
• PI Model 100%
• Computer Vision 100%
• Biologically Inspired Features 100%
• R-model 100%
• Face Identification 100%
• Poggio 100%
• Object Recognition Task 50%
• Primates 50%
• Face Recognition 50%
• Performance Level 50%
• Functional Magnetic Resonance Imaging 50%
• Imaging Experiment 50%
• Object Recognition 50%
• Biologically Plausible 50%
• International Conference 50%
• Electrophysiology 50%
• Pattern Recognition 50%
• Problem Recognition 50%
• Feedforward Model 50%
• Expression Recognition 50%
• Visual Object Recognition 50%
• Model Features 50%
• Vision Recognition 50%
• FERET 50%
• Face Expression 50%
• Histogram of Gradient Feature 50%
• High-level Features 50%
• Facial Expression Recognition 50%
• Local Binary Pattern 50%
• Low-level Features 50%

Computer Science

• Object Recognition 100%
• Face Processing 100%
• Expression Recognition 66%
• Face Identification 66%
• Visual Feature 66%
• Computer Vision 66%
• Facial Expression 33%
• Recognition Problem 33%
• Electrophysiology 33%
• Pattern Recognition 33%

Neuroscience

• Face 100%
• Face Perception 25%
• Functional Magnetic Resonance Imaging 25%
• Electrophysiology 25%
• Pattern Recognition 25%