• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.10
• /
• pp.43-50
• /
• 1997

In this paper, we present a method that can recognize face images by use of a sparse population code that is a learning model about a receptive fields of the simple cells in the primary visual cortex. Twenty front-view facial images form twenty persons were used for the training process, and 200 varied facial images, 20 per person, were used for test. The correct recognition rate was 100% for only the front-view test facial images, which include the images either with spectacles or of various expressions, while it was 90% in average for the total input images that include rotated faces. We analyzed the effect of nonlinear functon that determine the sparseness, and compared recognition rate using the sparese population code with that using eigenvectors (eigenfaces), which is compact code that makes contrast with the sparse population code.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2590-2606
• /
• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.