A Prototype for a Multimodal Biometric Security System Based on Face and Audio Signatures
Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra secure and reliable, due to the presence of multiple and independent verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of The establishment of the identity of a person in a reliable and time efficient manner is of paramount importance as the society is becoming increasingly dependent on the use of information technology for everyday tasks. Of the many automatic identification technologies, the methods based on biometrics have gained considerable attention due to its robustness as well as reliability. Biometrics is a measurable distinctive physical characteristic or personal trait that can be used to identify an individual or to verify the claimed identity of an individual. Even though, the biometric identification systems out perform peer technologies, the unimodal biometric systems have to contend with a variety of problems, namely, noisy data, intra class variations, restricted degrees of freedom, non universality and spoof attacks. Many of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidences presented by multiple sources of information. Using human face as a key to security, the biometrics face recognition technology has received significant attention in the past several years. Face biometrics is used for a wide variety of applications in both law enforcement as well as non law enforcement. Facial recognition records the spatial geometry of distinguishing features of the face. As compared with other biometrics systems using fingerprint/palmprint and iris, face recognition has a distinct advantage because face images can be captured from a distance without touching the person being identified. Voice of a person holds certain unique characteristics which can be utilized for personal authentication. Voice is a very intuitive behavioural and ubiquitous biometric which can be captured by modern personal computer. Multimodal biometric technology uses more than one biometric identifier to compare the identity of the person. A video image of a person speaking a pass phrase can be used in combination for authentication, which is going to make the identification system more reliable. In the present study, a prototype has been implemented which incorporates both face as well as speech for identification purposes. 2. SPEAKER RECOGNITION Speaker recognition is referred as recognizing person from their voices. Two individuals sounds are not identical because of the physical differences such as their vocal tract shapes, larynx sizes etc and also due to characteristic manner of speaking like the accent, rhythm, pronunciation pattern etc. The process of speaker identification can be divided into two main phases, namely, the training phase or enrolment phase and the testing phase or identification phase. During the speaker enrolment phase or the training mode, speech samples that contain the discriminating features are collected from the speakers and feature vectors are formed which are used to train the model. In the recognition phase, the feature vectors extracted from the unknown person's utterance are compared against the model in the system database to find the similarity score, for taking a decision. Feature selection is of great importance in speech recognition, as accuracy is highly dependent on the type and number of features used. Features have been computed from the spectrum of the speech signal and relates directly to some perceptual characteristics of sound, such as loudness, pitch, etc. Most of the features are generated from the spectrogram on a frame by frame basis.