Yongwha Chung

Automatic alignment of fingerprint features for fuzzy fingerprint vault

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. This construct aims to secure critical data(e.g., secret encryption key) with the fingerprint data in a way that only the authorized user can access the secret by providing the valid fingerprint, and some implementations results for fingerprint have been reported. However, all the previous results assumed that fingerprint features were pre-aligned, and automatic alignment in the fuzzy vault domain is a challenging issue. In this paper, we perform the automatic alignment of fingerprint features by using the geometric hashing technique which has been used for model-based object recognition applications. Based on the preliminary experimental results, we confirm that the proposed approach can align fingerprint features automatically in the domain of the fuzzy vault and can be integrated with any fuzzy fingerprint vault systems.

Performance evaluation of watermarking techniques for secure multimodal biometric systems

In this paper, we describe various watermarking techniques for secure user verification in the remote, multimodal biometric systems employing both fingerprint and face information, and compare their effects on user verification and watermark detection accuracies quantitatively. To evaluate the performance of watermarking for multimodal biometric systems, we first consider possible two scenarios – embedding facial features into a fingerprint image and embedding fingerprint features into a facial image. Additionally, to evaluate the performance of dual watermarking for secure biometric systems, we consider another two scenarios – with/without considering the characteristics of the fingerprint in embedding the dual watermark. Based on the experimental results, we confirm that embedding fingerprint features into a facial image can provide superior performance in terms of the user verification accuracy. Also, the dual watermarking with considering the characteristics of the fingerprint in embedding the dual watermark can provide superior performance in terms of the watermark detection accuracy.

Secure remote fingerprint verification using dual watermarks

As user authentication by using biometric information such as fingerprint has been widely accepted, there has been a growing interest in protecting the biometric information itself against external attackers. In this paper, we propose a dual watermarking technique to protect fingerprint images in transmission/storage. As the proposed dual watermarking technique provides both robustness and fragileness with the embedded watermarks, it can guarantee the integrity of the fingerprint image transmitted and/or stored. In particular, when the embedding locations for fragile watermarks are selected, we consider the ridge information of the fingerprint images to avoid possible interference between the robust watermark detection and fingerprint verification systems. Based on experimental results, we confirm that our dual watermarking technique can detect the robust watermark accurately and avoid any significant degradation in the accuracy of fingerprint verification.

Hiding biometric data for secure transmission

In this paper, we describe biometric watermarking techniques for secure user verification on the remote, multimodal biometric system employing both fingerprint and face information, and compare their effects on verification accuracy quantitatively. To hide biometric data with watermarking techniques, we first consider possible two scenarios. In the Scenario 1, we use a fingerprint image as a cover work and hide facial features into it. On the contrary, we hide fingerprint features into a facial image in the Scenario 2. Based on the experimental results, we confirm that the Scenario 2 is superior to the Scenario 1 in terms of the verification accuracy of the watermarked image.

Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods.

A Cost-Effective Pigsty Monitoring System Based on a Video Sensor

Automated activity monitoring has become important in many applications. In particular, automated monitoring is an important issue in large-scale management of group-housed livestock because it can save a significant part of farm workers’ time or minimize the damage caused by livestock problems. In this paper, we propose an automated solution for measuring the daily-life activities of pigs by using video data in order to manage the group-housed pigs. Especially, we focus on the circadian rhythm of group-housed pigs under windowless and 24-hour light-on conditions. Also, we derive a cost-effective solution within the acceptable range of quality for the activity monitoring application. From the experimental results with the video monitoring data obtained from two pig farms, we believe our method based on circadian rhythm can be applied for detecting management problems of group-housed pigs in a cost-effective way.

Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution.

A Survey and Proposed Framework on the Soft Biometrics Technique for Human Identification in Intelligent Video Surveillance System

Biometrics verification can be efficiently used for intrusion detection and intruder identification in video surveillance systems. Biometrics techniques can be largely divided into traditional and the so-called soft biometrics. Whereas traditional biometrics deals with physical characteristics such as face features, eye iris, and fingerprints, soft biometrics is concerned with such information as gender, national origin, and height. Traditional biometrics is versatile and highly accurate. But it is very difficult to get traditional biometric data from a distance and without personal cooperation. Soft biometrics, although featuring less accuracy, can be used much more freely though. Recently, many researchers have been made on human identification using soft biometrics data collected from a distance. In this paper, we use both traditional and soft biometrics for human identification and propose a framework for solving such problems as lighting, occlusion, and shadowing.

Protecting Secret Keys with Fuzzy Fingerprint Vault Based on a 3D Geometric Hash Table

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications such as home networks. This is also true for new authentication architectures known as crypto-biometricsystems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. In this paper, we propose an approach to provide both the automatic alignment of fingerprint data and higher security by using a 3D geometric hash table. Based on the experimental results, we confirm that the proposed approach of using the 3D geometric hash table with the idea of the fuzzy vaultcan perform the fingerprint verification securely even with one thousand chaff data included.

Automatic Detection of Cow's Oestrus in Audio Surveillance System.

Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.