Hyobin Lee

Biometric Image Authentication using Watermarking

In this paper, we propose an invertible authentication watermarking algorithm which can detect block-wise malicious manipulations in biometric images. Our method uses an invertible watermark that can also detect manipulated area simultaneously. Virtually all watermarking schemes introduce a small amount of irrecoverable distortion in original biometric images. But our new method is invertible in the sense that, if the data is deemed authentic, distortion due to authentication can be removed if it becomes necessary to obtain the original biometric image. In our method two watermarks are embedded into biometric image. The first one is based on the conventional method which can completely remove the distortion due to authentication if the data is deemed authentic. The second one can detect the block-wise malicious manipulation using the cyclic redundancy check (CRC) concept in the image block. Our proposed method can classify a test image into two types; authentic, and manipulated. Also, this technique provides cryptographic strength when verifying image integrity because the probability of making an undetectable modification to the image can be directly related to a secure cryptographic element, such as a hash function

Robust Face Recognition by Fusion of Visual and Infrared Cues

This paper proposes a robust face recognition method by fusing images acquired from visual and infrared (IR) sensors. Although current 2D image face recognition systems have reached a certain level of maturity, the performance of these systems has been limited by external conditions such as pose, expression and illumination. To alleviate some of these problems, infrared sensor based methods have been suggested. These methods showed very good performance when used in illumination variation environments. However, one of the main drawbacks of using IR sensors for recognition is that they are very sensitive to ambient temperatures. To solve this problem, we suggest a face recognition method that fuses both visual and IR techniques. We developed a visual and IR face image database with photographs taken under a wide range of imaging conditions. With this database, we are able to produce full combinations of comparative experiments in the field of face recognition. Our experiments showed that when using both modalities, results are far better in terms of recognition performance than when using only one modality. The overall average performance was observed to have improved under all imaging conditions

Invertible watermarking algorithm with detecting locations of malicious manipulation for biometric image authentication

In this paper, we present a new method for authentication of biometric images. Our method uses an invertible watermark that can also detect malicious manipulations simultaneously. While virtually all watermarking schemes introduce a small amount of non-invertible distortion in original biometric images, our new method is invertible in the sense that, if the data is deemed authentic, distortion due to authentication can be removed if it becomes necessary to obtain the original biometric image. This technique provides cryptographic strength when verifying image integrity because the probability of making an undetectable modification to the image can be directly related to a secure cryptographic element, such as a hash function. Also, if the biometric image is manipulated, the positions of intentional manipulation can be clearly identified.

Face detection using multi-modal features

In this paper, propose efficient methods for reducing false detection rate in face detection procedure when images apply face recognition and face entertainment from web or personal photo. Previous detection methods focused on accurate face detection. So, false detection rate makes a problem when put to practical use. This problem can overcome to use additional information when face detection. Proper information is color. But real color-RGB information is not adequate for face detection. Because RGB information is sensitive to light variations. Thus need to transfer coordination properly to use. Transferred channels are defined with genetic algorithm. After that make a model of facial skin color to score face candidate is real face or not. Propose method can dramatic reduce false detection rate when face detection procedure.

Fingertips detection and tracking based on active shape models and an ellipse

Detecting and tracking fingertips are significant techniques for recognizing hand gestures. Many patterns and skin color information are used to extract and trace features [1, 2]. However, to find correct shapes is difficult and there is limitation to express the diversity of models. In this paper, we describe a method of detecting and tracking fingertips based on Active Shape Models (ASMs) and an ellipse equation. ASM is an effective tool to obtain features of a specific object in images by using a trained model. The ellipse equation helps a template follow the object without skin color information. We show how well fingertips are extracted and traced through some experiments.

Reversible watermarking with localization for biometric images

In this paper, we propose a novel reversible data hiding algorithm, which can recover the original image if it is deemed authentic or detect the block-wise malicious manipulation if it is classified as manipulated. We explore the strong spatial correlation of neighboring pixels in digital images to achieve very high embedding capacity and keep the distortion low. Also, this technique provides cryptographic strength when verifying image integrity because the probability of making undetectable modifications to the image is directly related to a secure cryptographic element, such as a hash function. The algorithm has been successfully applied to a wide range of images, including commonly used images, biometric images, texture images, and aerial images. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

An Encoding Scheme for Robust Face Recognition

Noticing that many available view-based methods are confined within rather limited operating conditions, in this work we Investigate Into whether external factors such as illumination, pose, expression and occlusion can be explicitly estimated and be used to boost the verification performance. Our approach consists of three steps: encoding the external factors, code estimation, and identity verification using the encoded data. Empirical experiments were performed on this method using the BERC database which contains both visual and infrared facial images. The experimental results show significant performance elevation from the proposed method, thus demonstrating the effectiveness of isolating the external factors.

Fusion of Visual and Infrared Images for Face Recognition

This paper proposes a robust face recognition method by fusing images acquired from visual and infrared (IR) sensors. Although current 2D image face recognition systems have reached a certain level of maturity, the performance of these systems has been limited by external conditions such as pose, expression and illumination. To alleviate some of these problems, infrared sensor based methods have been suggested. These methods showed very good performance when used in illumination variation environments. However, one of the main drawbacks of using IR sensors for recognition is that they are very sensitive to ambient temperatures. To solve this problem, we suggest a face recognition method that fuses both visual and IR techniques. We developed a visual and IR face image database with photographs taken under a wide range of imaging conditions. With this database, we are able to produce full combinations of comparative experiments in the field of face recognition. Our experiments showed that when using both modalities, results are far better in terms of recognition performance than when using only one modality. The overall average performance was observed to have improved under all imaging conditions

Robust design of face recognition systems

Currently, most face recognition methods provide a number of parameters to be optimized, leaving the selection and optimization of the right parameter set is necessary for the implementation. The choice of the right parameter set that is suitable for a rich enough class of input faces in pose and illumination variations is, however, quite difficult. We propose robust parameter estimation, using the Taguchi method, when applied to 2nd order mixture of eigenfaces method that allows effective (near optimal) performance under pose and illumination variations. A number of experimental results confirm the improvement (via robustness) vis-‘a-vis conventional parameter estimation methods, and these methods promise a solution to the design of efficient parameter sets that support many multi-variable face recognition systems.