S. Tsutsi

Bio: S. Tsutsi is an academic researcher. The author has contributed to research in topics: Artificial intelligence & Fingerprint recognition. The author has an hindex of 1, co-authored 1 publications receiving 331 citations.

Intelligent biometric techniques in fingerprint and face recognition

Abstract: Introduction to Fingerprint Recognition, U. Halici, L.C. Jain, and A. Erol Fingerprint Feature Processing Techniques and Poroscopy, A.R. Roddy and J.D. Stosz Fingerprint Sub-Classification: A Neural Network Approach, G.A. Drets and H.G. Leljecstroem A Gabor Filter-Based Method for Fingerprint Identification, Y. Hamamoto Minutiae Extraction and Filtering from Gray-Scale Images, D. Maio and D. Maltoni Feature Selective Filtering for Ridge Extraction, A. Erol, U. Halici, and G. Ongun Introduction to Face Recognition, A.J. Howell Neural Networks for Face Recognition, A.S. Pandya and R.R. Szabo Face Unit Radial Basis Function Networks, A.J. Howell Face Recognition from Correspondence Maps, R.P. Wurtz Face Recognition by Elastic Bunch Graph Matching, L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg Facial Expression Synthesis Using Radial Basis Function Networks, I. King and X.Q. Li Recognition of Facial Expressions and Its Application to Human Computer Interaction, T. Onisawa and S. Kitazake

Fingerprints and Other Ridge Skin Impressions

Abstract: Since its publication, the first edition of Fingerprints and Other Ridge Skin Impressions has become a classic in the field. This second edition is completely updated, focusing on the latest technology and techniques—including current detection procedures, applicable processing and analysis methods—all while incorporating the expansive growth of literature on the topic since the publication of the original edition. Forensic science has been challenged in recent years as a result of errors, courts and other scientists contesting verdicts, and changes of a fundamental nature related to previous claims of infallibility and absolute individualization. As such, these factors represent a fundamental change in the way training, identifying, and reporting should be conducted. This book addresses these questions with a clear viewpoint as to where the profession—and ridge skin identification in particular—must go and what efforts and research will help develop the field over the next several years. The second edition introduces several new topics, including Discussion of ACE-V and research results from ACE-V studies Computerized marking systems to help examiners produce reports New probabilistic models and decision theories about ridge skin evidence interpretation, introducing Bayesnet tools Fundamental understanding of ridge mark detection techniques, with the introduction of new aspects such as nanotechnology, immunology and hyperspectral imaging Overview of reagent preparation and application Chapters cover all aspects of the subject, including the formation of friction ridges on the skin, the deposition of latent marks, ridge skin mark identification, the detection and enhancement of such marks, as well the recording of fingerprint evidence. The book serves as an essential reference for practitioners working in the field of fingermark detection and identification, as well as legal and police professionals and anyone studying forensic science with a view to understanding current thoughts and challenges in dactyloscopy.

Fingerprint classification: a review

Abstract: Biometrics is the automatic identification of an individual that is based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is currently the subject of intense research by both private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem. Finally, it presents empirical results from the state of the art fingerprint classification systems that have been tested using the NIST Special Database 4.

A biometric identification system based on eigenpalm and eigenfinger features

Abstract: This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

Fingerprint classification based on extraction and analysis of singularities and pseudoridges

Abstract: In this paper, we introduce a new approach to fingerprint classification based on both singularities and traced pseudoridge analysis. Since noise exists in most of the fingerprint images including those in the NIST databases which are used by many researchers, it is difficult to get the correct number and position of the singulairities such as core or delta points which are widely used in current structural classification methods. The problem is we may miss the true singular points and/or get false singular points due to the poor quality of fingerprint images. Classification based on exact pair of singulairities will fail in such conditions. With the help of the pseudoridge tracing and analysis of the traced curve, our method does not rely on the extraction of the exact number and positions of the true singular points, thus improving the classification accuracy. This method has been tested on the NIST-4 fingerprint database. For the 4000 images in this database, the classification accuracy is 95.3% with 11.8% reject rate for 4-class problem (combining Arch and Tented Arch as one class).