Gualberto Aguilar

Fingerprint Recognition

Fingerprint recognition is one of the most popular and successful methods used for person identification, which takes advantage of the fact that the fingerprint has some unique characteristics called minutiae; which are points where a curve track finishes, intersect with other track or branches off. Biometric identification systems using fingerprints patterns are called AFIS (Automatic Fingerprint Identification System). In this paper a novel method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters to enhancement the fingerprint image was captured using a UareU 4000 fingerprint reader of Digital Person, Inc.

Multimodal biometric system using fingerprint

Fingerprint recognition is one of the most popular methods used for identification with greater degree of success. The fingerprint has unique characteristics called minutiae, which are points where a curve track finishes, intersect or branches off. Identification systems using fingerprints biometric patterns are called AFIS (Automatic Fingerprint Identification System). In this work a method multi-biometric is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters to enhancement the image and later a novel stage of recognition using Local Features and Statistical Parameters.

Alaryngeal Speech Enhancement Using Pattern Recognition Techniques

An alaryngeal speech enhancement system is proposed to improve the intelligibility and quality of speech signals generated by an artificial larynx transducer (ALT). Proposed system identifies the voiced segments of alaryngeal speech signal, by using pattern recognition methods, and replaces these by their equivalent voiced segments of normal speech. Evaluation results show that proposed system provides a fairly good improvement of the quality and intelligibility of ALT generated speech.

Fingerprint verification applying invariant moments

Traditional security systems use passwords or ID cards have been used to moderate access to restricted systems, but these kind of systems have a poor performance because the security can be easily breached. Based in this disadvantage, the biometrics systems have a great popularity, the mains biometrics systems are: face recognition, iris recognition, voice recognition, fingerprint recognition and sinning recognition. The fingerprint recognition is the oldest method used to recognition or verification of person. Our proposed a people recognition system with verification by invariant moments using two methodologies for the fingerprint enhancement. The goal in this work is to get a robust system in security issues. In this work a method for fingerprint verification is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters by image enhancement, both methods are first applied separately and later on an algebraic sum is done to obtain a single output. After that, a thinning algorithm is applied to get an image with the minimum thickness of 1 pixel. After this thinning algorithm, we apply an algorithm to look minutiae using a window of 3 by 3 pixels to scan the image. In this work we extract two types of minutiae, bifurcation and ending. Then, the feature vector is generated with the distance between minutiae, angle between minutiae and coordinates. In the recognition stage using the coordinates from the minutiae position on the image a comparison is done. After that, apply a verification stage using the invariant moments. With the invariant moments values a comparison is done. The comparison was done using the values obtained for the images into database and the test image for to get the output. The results obtained in this research are better when we used FFT and Gabor filters algorithms to image enhancement than we used separately.

Speech enhancement of voice produced by an electronic larynx

Transcutaneous artificial larynx (TAL) has been available for over the last 40 years for patients who have had laryngectomies. However their use has several problems since the voice produced by these transducers is of low quality, unnatural and degraded by a steady background noise due to the leakage of acoustic energy from the TAL, its interface with the neck and the surrounding neck tissue, which gives a poorly intelligible and annoying result. In order to significantly enhance the speech as well as to improve the intelligibility of the TAL generated speech, a laryngectomyzed speech enhancement system, is developed in which the voiced parts are determined, by using a pattern recognition stage, and replaced by equivalent voiced parts of normal speech. The evaluation results show that a significant improvement of the artificial larynx generated speech quality and intelligibility is achieved.

GMM vs SVM for Face Recognition and Face Verification

The security is a theme of active research in which the identification and verification identity of persons is one of the most fundamental aspects nowadays. Face recognition is emerging as one of the most suitable solutions to the demands of recognition of people. Face verification is a task of active research with many applications from the 80’s. It is perhaps the biometric method easier to understand and non-invasive system because for us the face is the most direct way to identify people and because the data acquisition method consist basically on to take a picture. Doing this recognition method be very popular among most of the biometric systems users. Several face recognition algorithms have been proposed, which achieve recognition rates higher than 90% under desirable’s condition (Chellapa et al., 2010; Hazem & Mastorakis, 2009; Jain et al., 2004; Zhao et al., 2003). The recognition is a very complex task for the human brain without a concrete explanation. We can recognize thousands of faces learned throughout our lives and identify familiar faces at first sight even after several years of separation. For this reason, the Face Recognition is an active field of research which has different applications. There are several reasons for the recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world and the need for face analysis and modeling techniques in multimedia data management and computer entertainment. Recent advances in automated face analysis, pattern recognition, and machine learning have made it possible to develop automatic face recognition systems to address these applications (Duda et al., 2001). This chapter presents a performance evaluation of two widely used classifiers such as Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) for classification task in a face recognition system, but before beginning to explain about the classification stage it is necessary to explain with detail the different stages that make up a face recognition system in general, to understand the background before using the classifier, because the stages that precede it are very important for the proper operation of any type of classifier.

Fingerprint Recongnition Using Espatial Minutae Information

A fingerprint is the visible impression or molded that papillary produces with the papillary crest contact in a surface. It depends on the conditions under which the fingerprint is made, and the support characteristics (plastics or soft matters in due conditions), however, it is an individual characteristic that is used as a main pattern for people identification. In this research the image enhancement is carry out using FFT (Fast Fourier Transform) and Gabor filters, the minutiae extraction approach is done to obtain a characteristic vector which has distance, angle and minutiae coordinates those vectors will be used for training and recognition.

Face Recognition Using Frequency Domain Feature Extraction Methods

The development of security systems based on biometric features has been a topic of active research during the last years, because the recognition of the people identity to access control is a fundamental issue in this day. Terrorist attacks happened during the last decade have demonstrated that it is indispensable to have reliable security systems in offices, banks, airports, etc.; increasing in such way the necessity to develop more reliable methods for people recognition. The biometrics systems consist of a group of automated methods for recognition or verification of people identity using the physical characteristics or personal behavior of the person under analysis. In particular, face recognition is a task that humans perform carry out routinely in their daily lives. Face recognition is the most common form human beings have of telling one another apart. Faces are universal, and they provide a means to differentiate individuals. An advantage of biometric face recognition compared to other biometric is the ability to capture a facial image with a camera from a distance and without the subject’s knowledge. The face recognition has been a topic of active research because the face is the most direct way to recognize the people. In addition, the data acquisition of this method consists, simply, of taking a picture with or without collaboration of the person under analysis, doing it one of the biometric methods with larger acceptance among the users. The face recognition is a very complex activity of the human brain. For example, we can recognize hundred of faces learned throughout our life and to identify familiar faces at the first sight, even after several years of separation, with relative easy. However it is not a simple task for a computer. Thus to develop high performance face recognition systems, we must to develop accurate feature extraction and classification methods, because, as happens with any pattern recognition algorithm, the performance of a face recognition algorithm strongly depends on the feature extraction method and the classification systems used to carry out the face recognition task. Thus several feature extraction methods for using in face recognition systems have been proposed during the last decades, which achieve high accurate recognition. Among the situations that drastically decrease the accuracy and that must be considered to develop high performance face recognition method we have: partial occlusion, illumination variations, size change, rotation and translation of the capture image, etc. To solve these problems several efficient feature extraction methods have been proposed, several of them

Frequency-Based Fingerprint Recognition

abstract Fingerprint recognition is one of the most popular methods used for identification with greater success degree. Fingerprint has unique characteristics called minutiae, which are points where a curve track ends, intersects, or branches off. In this chapter a fingerprint recognition method is proposed in which a combination of fast Fourier transform (FFT) and Gabor filters is used for image enhancement. A novel recognition stage using local features for recognition is also proposed. Also a verification stage is introduced to be used when the system output has more than one person.