Abstract: A user authentication method and apparatus using a face image are provided. The method includes transforming a face image in a normalized spatial domain into frequency-domain data, extracting valid transform coefficients from the frequency-domain data based on energy-concentrated region information, extracting a feature vector from the extracted valid transform coefficients, and performing user authentication by comparing the extracted feature vector with a previously registered feature vector. Accordingly, it is possible to perform user authentication using a face image while using a minimum data dimension, thereby improving the speed and precision thereof.

Abstract: Provided a secure pattern recognition method. The method includes: receiving data and generating a probe by converting the received data into a template for pattern recognition; accessing a gallery that is a template registered and stored in advance; determining a region to which the probe belongs and obtaining the center point of the region; obtaining a hash value of the center point and coordinate of the probe; and determining whether or not the hash value of the center point and a hash value stored in the gallery are equal and determining whether or not the probe and the gallery are classified into the same class by calculating whether or not the coordinate of the probe is inside a decision boundary configured with thresholds on the basis of the coordinates of the center point.

Abstract: Provided are a method and an apparatus for accurately detecting positions of eyes in an input face image. The method includes extracting a symmetric face region from a face image based on magnitude and phase of gradient in each pixel of the face image; detecting available eye positions based on brightness information of the extracted face region; verifying regions around each of the detected eye positions with a classifier which determines whether an input image is an eye image using information obtained by supervised learning on sample images. According to the present invention, faulty eye detection is prevented by removing obscuring elements such as illumination, glasses and hair from an input face image. Also, eye coordinates can be extracted more accurately using an eye classifier.

Abstract: A face recognition and apparatus are provided. According to the method, an SVM classifier is created through machine learning on the basis of a degree of similarity of a divided facial image, and a facial image to be authenticated is normalized to a predetermined size using a center between two eyes. The normalized image is divided into more than one image in horizontal and vertical directions, respectively. Next, predetermined characteristic vectors from the divided images are extracted and a similarity vector based on a degree of similarity with respect to a registered characteristic vector is created. The similarity vector is input to the SVM classifier, so that authentication is performed.

Abstract: In a system for identifying an object having a bar code symbology, a measurement and input unit measure a height and a length of the object and input the measured values into the system. An object image and search region image storage unit stores an image of the object and images of at least two sub blocks from the object. A search region setting unit sets a size of the sub blocks. A region of interest (ROI) generation unit sets a weight and a characteristic value of the sub block. A bar code symbology identification unit searches vertically a bar code region from the ROI and generates a bar code edge region. An information interpretation unit interprets object information corresponding to the bar code edge region. A result process and storage unit processes and stores the interpreted object information.

Abstract: In a system for identifying an object having a bar code symbology, a measurement and input unit measure a height and a length of the object and input the measured values into the system. An object image and search region image storage unit stores an image of the object and images of at least two sub blocks from the object. A search region setting unit sets a size of the sub blocks. A region of interest (ROI) generation unit sets a weight and a characteristic value of the sub block. A bar code symbology identification unit searches vertically a bar code region from the ROI and generates a bar code edge region. An information interpretation unit interprets object information corresponding to the bar code edge region. A result process and storage unit processes and stores the interpreted object information.

Abstract: The present invention relates to an apparatus and method for real-time automatically taking the length, width and height of a rectangular object that is moved on a conveyor belt. The method of taking the dimensions of a 3D object, the method comprising the steps of: a) obtaining an object image having the 3D object; b) detecting all edges within a region of interest of the 3D object; c) extracting line segments from the edges of the 3D object and then extracting features of the 3D object from the line segments; and d) generating 3D models based on the features of the 3D object and taking the dimensions of the 3D object from the 3D models.