Automatic identity recognition systems : a review

Rapidly changed computer technology and fast growth of communication ways, makes everyday work easy and managed. Technology takes place everywhere, in business, education, market, security... etc. However, communication between human and these technologies become the main concern of many research areas, especially for developing automatic identity recognition systems. However, biometric technologies are among the most important technologies used in this area. Biometric technology refers to the automatic identity recognition using physical or behavioral traits associated with him/her. Using biometrics, it is possible to establish physiological-based systems that depend on physiological characteristics such as fingerprint, face recognition, DNA... etc, or behavioral-based systems that depend on behavioral characteristics such as gait, voice ...etc, or even combining both of them in one system. Therefore, biometrics technologies can be excellent candidates for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. In addition, biometric technologies are flexible enough to be combined with other tools to produce more secure and easier to use verification system

Arabic automatic continuous speech recognition systems

MSA is the current formal linguistic standard of Arabic language, which is widely taught in schools and universities, and often used in the office and the media. MSA is also considered as the only acceptable form of Arabic language for all native speakers [I]. As recently, the research community has witnessed an improvement in the performance of ASR systems, there is an increasingly widespread use of this technology for several languages of the world. Similarly, research interests have grown significantly in the past few years for Arabic ASR research. It is noticed that Arabic ASR research is not only conducted and investigated by researchers in the Arab world, but also by many others located in different parts of the \vorld especially the western countries

Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus

This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world. The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively

Speaker’s variabilities, technology and language issues that affect automatic speech and speaker recognition systems

Automatic Speech Recognition (ASR) is gammg its importance due to the vast growth generally in technology and computing in specific. From industrial perspective, computers, laptops, and mobile devices nowadays have the ASR support embedded into the operating system. From academia on the other hand, there are many research efforts being conducted addressing this technology in order to contribute to its state-of-the-art. On the other hand, speaker recognition systems are also growing due to various threats, therefore, these systems are mostly meant for security purpose

HUMAN FACE DETECTION IN COLOR IMAGES

In this paper we have used a simple and efficient color-based approach to segment human skin pixels from background, using a 2D histogram-based approach as a preprocess stage for human face detection. For skin segmentation, a total of 446,007 skin samples from the training set is manually cropped from the RGB color images, to calculate three lookup tables based on the relationship between each pair of the triple components (R, G, B). Derivation of skin classifier rules from the lookup tables are based on how often each attribute value (interval) occurs, and their associated certainty values. For face detection, we assume the face-appearance as blob-like, and that the face has an approximately elliptical shape. Accordingly, an ellipse-fitting algorithm is appropriate, which is based on statistical moments, and those blobs that have an elliptical shape are retained as face candidates.Skin segmentation, histogram-based approach, lookup table, skin classifier, ellipse fitting, face detection, feature-based approach

Performance evaluation of the combination of Compacted Dither Pattern Codes with Bhattacharyya classifier in video visual concept depiction

High dimensionality and multi-feature combinations can have negative effect on visual concept classification. In our research, we formulated a new compacted form which is Compacted Dither Pattern Code (CDPC) as a chromatic syntactic feature for visual feature extraction. The effectiveness of CDPC with Bhattacharyya classifier for irregular shapes based visual concepts depiction is reported in this paper. The proposed technique can reduce feature space and computational complexity while maintaining visual data mining and retrieval accuracy in high standard. Our system was empowered with Bhattacharyya classifier which has improved efficiency by considering one numeric value which is the Bhattacharyya coefficient. Experiments were conducted on various combinations and compared with different visual descriptors and classifiers. The first experiment illustrates the comparison of the CDPC based results with well known feature space reduction classes. The second and third experiments demonstrate the effectiveness of our approach with multiple perspectives of performance measures including various concepts