This letter proposes a background subtraction method for Bayer-pattern image sequences. The proposed method models the background in a Bayer-pattern domain using a mixture of Gaussians (MoG) and classifies the foreground in an interpolated red, green, and blue (RGB) domain. This method can achieve almost the same accuracy as MoG using RGB color images while maintaining computational resources (time and memory) similar to MoG using grayscale images. Experimental results show that the proposed method is a good solution to obtain high accuracy and low resource requirements simultaneously. This improvement is important for a low-level task like background subtraction since its accuracy affects the performance of high-level tasks, and is preferable for implementation in real-time embedded systems such as smart cameras.

This letter proposes an efficient and robust background compensation method for pan-tilt-zoom cameras. The proposed method approximates the relation between consecutive images to a three-parameter similarity transformation, which is separable in horizontal and vertical axes, and extracts and matches 1-D features that are local minima and maxima of intensity projection profiles in each axis. These correspondences are used to estimate transformation parameters via an outlier rejection approach. Experimental results show that the proposed method is more robust with respect to blurring effects and moving object proportion while dramatically decreasing computational costs compared to previous methods.

This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new features—the normalized mean and the standard deviation of the horizontal edge projection histogram—to reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200 000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.

Many age estimation methods have been proposed for various applications such as Age Specific Human Computer Interaction (ASHCI) system, age simulation system and so on. Because the performance of the age estimation is greatly affected by the aging feature, the aging feature extraction from facial images is very important. The aging features used in previous works can be divided into global and local features. As global features, Active Appearance Models (AAM) was mainly used for age estimation in previous works. However, AAM is not enough to represent local features such as wrinkle and skin. Therefore, the research about local features is required. In previous works, local features were generally used to determine age group rather than detailed age, and the comparative studies about various local features extraction methods were not conducted. In this paper, the performances of sobel filter, difference image between original and smoothed image, ideal high pass filter (IHPF), gaussian high pass filter (GHPF), Haar and Daubechies discrete wavelet transform (DWT) are compared for extracting local features and detailed age estimation is performed by Support Vector Regression (SVR) on BERC and PAL aging database. The experimental results show that local features can be used for detailed age estimation and GHPF gives a better performance than other methods.

This paper proposes a face occlusion verification method for an automated teller machine (ATM) application. The proposed method mainly consists of three steps. Firstly, a head and shoulder shape is detected by applying B-spline active contour to motion edges. This motion edge is generated by a kurtosis-based frame selection and distance transformation-based motion edge detection. Secondly, a face area is estimated by fitting an ellipse to the detected head and shoulder shape. Finally, occlusion of the face area is determined by measuring skin color area ratio (SCAR) of whole face area and facial component areas. Experimental results show that the proposed head and shoulder detection method has 94.8% detection rate even though there are various types of severe occlusions in faces, and the proposed occlusion verifier has 86.7% verification rate.

Touchless fingerprint sensing technologies have been explored to solve problems in touch-based sensing techniques because they do not require any contact between a sensor and a finger. While they can solve problems caused by the contact of a finger, other difficulties emerge such as a view difference problem and a limited usable area due to perspective distortion. In order to overcome these difficulties, we propose a new touchless fingerprint sensing device capturing three different views at one time and a method for mosaicing these view-different images. The device is composed of a single camera and two planar mirrors reflecting side views of a finger, and it is an alternative to expensive multiple-camera-based systems. The mosaic method can composite the multiple view images by using the thin plate spline model to expand the usable area of a fingerprint image. In particular, to reduce the affect of perspective distortion, we select the regions in each view by minimizing the ridge interval variations in a final mosaiced image. Results are promising as our experiments show that mosaiced images offer 29% more true minutiae and 28% larger good quality area than one-view, unmosaiced images. Also, when the side-view images are matched to the mosaiced images, it gives more matched minutiae than matching with one-view frontal images. We expect that the proposed method can reduce the view difference problem and increase the usable area of a touchless fingerprint image. Furthermore, the proposed method can be applied to other biometric applications requiring a large template for recognition.

This paper proposes a novel light-stripe-projection-based target position-designation method for an intelligent parking assist system, providing an economical free-space-based target position-designation method for poorly lit indoor parking spaces without degradation of the external appearance when integrated into a vehicle's body. The proposed method can be applied by adding a low-cost light plane projector to existing backward-parking cameras. Light stripe features are detected by the difference between an image with the light plane projector on and off. Three-dimensional information of the parking area is reconstructed using the light-stripe-projection theory. By orientation normalization and depth-map construction, free spaces are detected, and the nearest is selected as the target position. Experimental results show that the proposed method can successfully designate the target position, in spite of poor lighting conditions and the black reflective surface of adjacently parked vehicles.

Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.

Recently, customers have shown a growing interest in parking aid products. A parking aid system consists of target position designation, path planning, and parking guidance by user interface or path tracking by active steering. For target position designation, various sensors and signal processing technologies have been tried. In the case of parallel parking, an ultrasonic sensor-based method plays a dominant role. In the case of perpendicular parking, a graphical user interface (GUI)-based method and a parking slot marking-based method have been commercialized. However, methods developed for the recognition of free parking space between vehicles have their respective drawbacks. This paper proposes a method for the recognition of free parking space between vehicles using scanning laser radar. This proposed method consists of range data preprocessing, corner detection, and target parking position designation. The authors developed a novel corner-detection method consisting of rectangular corner detection and round corner detection. The newly developed corner detection is unaffected by cluster orientation and the range data interval and is robust to noise. Experimental results showed that even in situations where other methods failed, the proposed scanning laser radar-based method could designate target parking position to viable free parking space. The recognition rate was 98.21%, and the average processing time was about 600 ms. Finally, it is argued that the proposed method will eventually be a practical solution because of the decreasing price of scanning laser radar and multiple-function integration strategies.

This paper investigates the effect of vehicle front height lowering operation of precrash dipping nose (PCDN) on pedestrians. Although PCDN was developed for vehicle-vehicle side crashes, there is a possibility that the range sensor for crash detection will fail to distinguish a group of pedestrians from a side-faced vehicle. For simulation-based investigation, a vehicle model, an air spring with PCDN actuator, a pedestrian, and active hood system (AHS) were modeled. Two vehicle models were made for a sedan and a sport utility vehicle (SUV), respectively. In all crash situations (frontal crash without AHS, frontal crash with AHS, and side crash without AHS), a falsely operated PCDN is expected to cause worse pedestrian injury. As a conclusion, we insist that PCDN should incorporate a pedestrian-recognition capability into its crash detection system to reduce the improper activation of its actuator. In addition, as AHS showed a good performance even in improper activation, we propose a complementary method that activates AHS when PCDN is activated. It is noteworthy that the increasing importance of pedestrian protection seems to enforce even a system developed for vehicle-vehicle crashes to be investigated from the perspective of pedestrian safety.

This paper proposes a recognizable-image selection algorithm for fingerprint-verification systems that use a camera embedded in a mobile device. A recognizable image is defined as the fingerprint image which includes the characteristics that are sufficiently discriminating an individual from other people. While general camera systems obtain focused images by using various gradient measures to estimate high-frequency components, mobile cameras cannot acquire recognizable images in the same way because the obtained images may not be adequate for fingerprint recognition, even if they are properly focused. A recognizable image has to meet the following two conditions: First, valid region in the recognizable image should be large enough compared with other nonrecognizable images. Here, a valid region is a well-focused part, and ridges in the region are clearly distinguishable from valleys. In order to select valid regions, this paper proposes a new focus-measurement algorithm using the secondary partial derivatives and a quality estimation utilizing the coherence and symmetry of gradient distribution. Second, rolling and pitching degrees of a finger measured from the camera plane should be within some limit for a recognizable image. The position of a core point and the contour of a finger are used to estimate the degrees of rolling and pitching. Experimental results show that our proposed method selects valid regions and estimates the degrees of rolling and pitching properly. In addition, fingerprint-verification performance is improved by detecting the recognizable images.

Cryptographic systems have been widely used in many information security applications. One main challenge that these systems have faced has been how to protect private keys from attackers. Recently, biometric cryptosystems have been introduced as a reliable way of concealing private keys by using biometric data. A fuzzy vault refers to a biometric cryptosystem that can be used to effectively protect private keys and to release them only when legitimate users enter their biometric data. In biometric systems, a critical problem is storing biometric templates in a database. However, fuzzy vault systems do not need to directly store these templates since they are combined with private keys by using cryptography. Previous fuzzy vault systems were designed by using fingerprint, face, and so on. However, there has been no attempt to implement a fuzzy vault system that used an iris. In biometric applications, it is widely known that an iris can discriminate between persons better than other biometric modalities. In this paper, we propose a reliable fuzzy vault system based on local iris features. We extracted multiple iris features from multiple local regions in a given iris image, and the exact values of the unordered set were then produced using the clustering method. To align the iris templates with the new input iris data, a shift-matching technique was applied. Experimental results showed that 128-bit private keys were securely and robustly generated by using any given iris data without requiring prealignment.

Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.

This paper proposes non-intrusive iris image capturing system, which consists of pan-tilt-zoom camera and light stripe projection. Light stripe projection provides the position of user. After panning according to user's position, AdaBoost-based face detection finds tilt angle. With user's position and tilt angle, zoom and focus position are initialized. User's position replaces 2D face search with ID face search. Exact zoom and focus position enable fast control and narrow search range. Consequently, experimental results show that proposed system can capture iris image within acceptable time.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.

For fast iris recognition, it is very important to capture the user's focused eye image at fast speed. Previous researchers have used the focusing method which has been applied to general landscape scenes without considering the characteristics of the iris image. So, they take much focusing time, especially in the case of the user with glasses. To overcome such problems, we propose a new iris image acquisition method to capture focused eye images at very fast speed based on corneal specular reflection. Experimental results show that the focusing time for both users with and without glasses averages 480 ms, and we conclude that our method can be used for the real-time iris recognition camera.

The Korean character is composed of several alphabets in two-dimensional formation and the total number of Korean characters exceeds eleven thousand. Therefore, the previous approaches to Korean cursive characters pay most of their attention to segmenting a character into alphabets accurately. However, it is difficult because the boundaries of alphabets are not apparent in most cases. We propose an alphabet-based method without assuming accurate alphabet segmentation. In the proposed method, a cursive character is segmented into substrokes by a set of segmenting conditions. Then it is matched with the reference substrokes generated from alphabet models and ligatures by segmenting and merging in the process of recognition. Among substrokes, a certain substroke can be either an alphabet itself a part of alphabet or a composite of the alphabet and ligature. We applied the proposed method to 5000 Korean characters and got the result of 83.4% for the first rank and 89.2% for the top 5 result candidates with the speed of 0.17 seconds on average per character on a PC which uses Intel Pentium 90 Mhz CPU.

A map consists of many lines, and the process to recognize an object in map is one to group lines that are consistent with the structure of the object. This paper proposes a novel method for recognizing roads and districts in land register maps. The lines that constitute an object are searched in a reduced search space by defining the some relations between lines, and objects are recognized by using the structural information.

It is important to use a better criterion in selection and discretization of attributes for the generation of decision trees to construct a better classifier in the area of pattern recognition in order to intelligently access huge amount of data efficiently. Two well-known criteria are gain and gain ratio, both based on the entropy of partitions. We propose in this paper a new criterion based also on entropy, and use both theoretical analysis and computer simulation to demonstrate that it works better than gain or gain ratio in a wide variety of situations. We use the usual entropy calculation where the base of the logarithm is not two but the number of successors to the node. Our theoretical analysis leads some specific situations in which the new criterion works always better than gain or gain ratio, and the simulation result may implicitly cover all the other situations not covered by the analysis.

A cursive Korean character consists of several Korean alphabets where connection is present within and among the alphabets. Recognition of Korean characters can be carried out by splitting each character into smaller primitives. Small line segments can be used as the primitives. But this approach requires too much processing time, for there can be many candidate references to be matched to one input character and each reference usually consists of too many primitives. In this paper, we propose an approach using structural curvature models to overcome the difficulties of using small line segments. These models are obtained by segmenting the input character at the points showing sudden change in direction, excessive rotation, etc. By doing this, rather larger and structural curve segments can be used as the basic primitives to be matched resulting in the savings of processing time and better recognition rate.

For signature verification, there can be a large number of useful features including pen-speed, pen-pressure, etc. However, all these features may not be useful or even be harmful for a given person when not enough number of training samples are allowed. Namely, for an optimal verification, each person requires a different subset of all the possible features. Finding an optimal subset for an individual needs too much combinatorial efforts. Instead, we propose an approach using all the same features for every person by assigning a different weight to each feature according to the nature of the person's signature.