S. Tachaphetpiboon

Bio: S. Tachaphetpiboon is an academic researcher from King Mongkut's University of Technology Thonburi. The author has contributed to research in topics: Fingerprint & Feature extraction. The author has an hindex of 4, co-authored 6 publications receiving 92 citations.

Fingerprint recognition using DCT features

Abstract: A method of fingerprint recognition based on the DCT features of a discrete image is proposed. Its performance is evaluated by the k-nearest neighbour (k-NN) classifier. Experimental results show the achievement of the proposed method in terms of the recognition rate and the low computational effort.

A fingerprint matching method using DCT features

Abstract: This paper proposes an extraction method of DCT features for fingerprint matching. In the features extraction process, a fingerprint image is quartered and transformed by the DCT. The standard deviation of the DCT coefficients in predefined areas is then calculated and used for fingerprint matching. The recognition rate of the proposed method is evaluated by the k-NN classifier. The results obtained are finally compared to the existing method based on the wavelet features. The processing time required in both features extraction and matching processes between two approaches are also compared. With our proposed method, both higher recognition rate and lower complexity can be achieved at the same time.

Fingerprint features extraction using curve-scanned DCT coefficients

Abstract: This paper proposes a fingerprint features extraction method using curve-scanned DCT coefficients. Generally, the fingerprint features contained in a fingerprint image, such as ridge line patterns and minutiae point, can be extracted from the DCT domain, and used for fingerprint matching. By considering the oscillate pattern contained in the top-left corner of DCT coefficients, we can divide those coefficients in curve-scanned fashion, extract DCT features from the divided DCT coefficients, and use them for matching purpose. To evaluate the proposed method, we use the k-NN classifier to measure the recognition rate, and compare the results obtained from our extraction method to that from the DWT based method [5] and the zigzag-scanned based method [6]. According to the results, the proposed method outperforms the other two, particularly in both recognition rate and processing time.

Digital watermarking for color images in hue-saturation-value color space

Abstract: This paper proposes a new watermarking scheme for color images, in which all pixels of the image are used for embedding watermark bits in order to achieve the highest amount of embedding. For watermark embedding, the S component in the hue-saturation-value (HSV) color space is used to carry the watermark bits, while the V component is used in accordance with a human visual system model to determine the proper watermark strength. In the proposed scheme, the number of watermark bits equals the number of pixels in the host image. Watermark extraction is accomplished blindly based on the use of a 3 × 3 spatial domain Wiener filter. The efficiency of our proposed image watermarking scheme depends mainly on the accuracy of the estimate of the original S component. The experimental results show that the performance of the proposed scheme, under no attacks and against various types of attacks, was superior to the previous existing watermarking schemes.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A Fingerprint Recognition Scheme Based on Assembling Invariant Moments for Cloud Computing Communications

Abstract: In cloud computing communications, information security entails the protection of information elements (e.g., multimedia data), only authorized users are allowed to access the available contents. Fingerprint recognition is one of the popular and effective approaches for priori authorizing the users and protecting the information elements during the communications. However, traditional fingerprint recognition approaches have demerits of easy losing rich information and poor performances due to the complex inputs, such as image rotation, incomplete input image, poor quality image enrollment, and so on. In order to overcome these shortcomings, in this paper, a new fingerprint recognition scheme based on a set of assembled invariant moment (geometric moment and Zernike moment) features to ensure the secure communications is proposed. And the proposed scheme is also based on an effective preprocessing, the extraction of local and global features and a powerful classification tool, thus it is able to handle the various input conditions encountered in the cloud computing communication. The experimental results show that the proposed method has a higher matching accuracy comparing with traditional or individual feature based methods on public databases.

A Joint FED Watermarking System Using Spatial Fusion for Verifying the Security Issues of Teleradiology

Abstract: Teleradiology allows transmission of medical images for clinical data interpretation to provide improved e-health care access, delivery, and standards. The remote transmission raises various ethical and legal issues like image retention, fraud, privacy, malpractice liability, etc. A joint FED watermarking system means a joint fingerprint/encryption/dual watermarking system is proposed for addressing these issues. The system combines a region based substitution dual watermarking algorithm using spatial fusion, stream cipher algorithm using symmetric key, and fingerprint verification algorithm using invariants. This paper aims to give access to the outcomes of medical images with confidentiality, availability, integrity, and its origin. The watermarking, encryption, and fingerprint enrollment are conducted jointly in protection stage such that the extraction, decryption, and verification can be applied independently. The dual watermarking system, introducing two different embedding schemes, one used for patient data and other for fingerprint features, reduces the difficulty in maintenance of multiple documents like authentication data, personnel and diagnosis data, and medical images. The spatial fusion algorithm, which determines the region of embedding using threshold from the image to embed the encrypted patient data, follows the exact rules of fusion resulting in better quality than other fusion techniques. The four step stream cipher algorithm using symmetric key for encrypting the patient data with fingerprint verification system using algebraic invariants improves the robustness of the medical information. The experiment result of proposed scheme is evaluated for security and quality analysis in DICOM medical images resulted well in terms of attacks, quality index, and imperceptibility.