Kwang-Baek Kim

Intelligent immigration control system by using passport recognition and face verification

This paper proposed the intelligent immigration control system that authorizes the traveler immigration and detects the forged passports by using automatic recognition of passport codes and the picture and face verification. The proposed system extracts and deskewes the areas of passport codes from the passport image. This paper proposed a novel ART algorithm creating the adaptive clusters to the variations of input patterns and it was applied to the extracted code areas for the code recognition. After compensating heuristically the recognition result, the detection of forged passport is achieved by using the picture and face verification between the picture extracted from passport image and the picture retrieved from the database based on the recognized codes. Due to the proposed ART algorithm and the heuristic refinement, the proposed system shows the relatively better performance.

Recognition of english business cards using enhanced hybrid network

In this paper, we proposed a novel method for the recognition of English business cards by using the projection method and the enhanced hybrid network. The recognition of business cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of business cards, and the feature areas including character strings are separated from the business card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method, the feature areas are split into the areas of individual characters. We also proposed the enhanced hybrid network that organizes the middle layer effectively by using the enhanced ART1 neural network adjusting the vigilance threshold dynamically according to the homogeneity between patterns. In the recognition phase, the proposed neural network is applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional neural network based recognition.

Facial Feature Extraction using Multiple Active Appearance Model

Active Appearance Model(AAM) is one of the facial feature extraction techniques. In this paper, we propose the Multiple Active Appearance Model(MAAM). Proposed method uses two AAMs. Each AAM trains using different training parameters. It causes that each AAM has different strong points. One AAM complements the weak points in the other AAM. We performed the facial feature extraction on the 100 images to verify the performance of MAAM. Experiment results show that MAAM gives more accurate results than AAM with less fitting iteration.

Image recognition using fractal parameters

Concerns the applications of fractal theory to image recognition and we propose the method that can enhance learning rate and recognition rate by using fractal parameters that are composed of input vectors for a neural network in an image recognition model. Fractal parameters with the properties of self-similarity and recursiveness can recover lossless original images through iterating processes. Therefore the original image can be implicitly represented and uniquely mapped by fractal parameters. The enhanced result is shown by computer simulations.