Familiar face recognition: a comparative study of a connectionist model and human performance

A connectionist simulation of familiar face recognition by humans, which incorporates aspects of the Bruce and Young cognitive model, is discussed in detail. The computer model employs three self-organising neural maps, whose input vectors are the eye region, the mouth region and a reduced resolution full-face image of the input grey-scale face images, together with a decision-making supervised multi-layer perceptron as the output network. A comparative set of experiments has been performed using this simulation and human subjects for the task of familiar face recognition. When the gender of the facial images is supplied to the simulation, the recall misclassifications of the simulation and the human subjects are statistically similar. A connectionist simulation of familiar face recognition by humans, which incorporates aspects of the Bruce and Young cognitive model, is discussed in detail. The computer model employs three self-organising neural maps, whose input vectors are the eye region, the mouth region and a reduced resolution full-face image of the input grey-scale face images, together with a decision-making supervised multi-layer perceptron as the output network. A comparative set of experiments has been performed using this simulation and human subjects for the task of familiar face recognition. When the gender of the facial images is supplied to the simulation, the recall misclassifications of the simulation and the human subjects are statistically similar.