Accession Number:

ADP011353

Title:

Characteristic Extraction of Face Using DWT and Recognition Based on Neural Networks

Descriptive Note:

Conference proceedings

Corporate Author:

DONGKANG COLL KWANGJU (KOREA) DEPT OF ELECTRONIC INFORMATION ENGINEERING

Report Date:

2000-07-01

Pagination or Media Count:

12.0

Abstract:

In this paper, we suggest how to segment the face when there is the man under complex environment, extracts the features from segmented the image and proposes a effective recognition system using the discrete wavelet transform DWT. This algorithm is proposed by following processes. First, two gray-level images is captured with 256 level of the size of 256 x 256 in constant illumination. We use a Gaussian filter to remove noise of input image and get a differential image between background and input image. Second, a mask is made from erosion and dilation process after binary of the differential image. Third, facial image is divided by projecting the mask into input image. Most characteristic information of human face is in eyebrow, eyes, nose and mouth. In the reason, the facial characteristic are detected after selecting local area including this area. Forth, detecting the characteristic of segmented face image, edge is detected with Sobel operator. Then, eye area and the center of face are searched by using horizontal and vertical components of edge. Characteristic area consists of eyes, a nose, a mouth, eye brows and cheeks, is detected by searching the edge of the image. Finally, characteristic vectors are extracted from performing DWT of this characteristic area and are normalized it between 1 and 1. Normalized vectors is used with input vector of neural network. Simulation results show recognition rate of 100 about learned image and 92 about test image.

Subject Categories:

  • Numerical Mathematics
  • Photography

Distribution Statement:

APPROVED FOR PUBLIC RELEASE