Development of a Real-Time Intelligent Biometric Face Detection and Recognition System in LabVIEW

Face detection and recognition plays a vital role with broad application in areas like crowd surveillance, security system, human co mputer interface, etc. In principle, biometric system is preferred for people identification due to its reliability and accuracy. The biggest challenge in face recognition arises when a real-time application system is designed for frontal and non-frontal images. The variations in face poses and expressions greatly impact the identification accuracy of a moving person. To circumvent this issue, in this paper, a real-time biometric system using face region is designed to detect and recognize a person in a pre-defined range using LabVIEW . Face region is propos ed to eliminate any physical contact with the system. Neura l Network (NN) is employed by training the face images in different distance and angle which allows this system to work for frontal and non- fron tal face recognition. Algorith ms in LabVIEW are developed to detect and extract the face region in a captured fra me which is then sent to NN for recognition process. Consecutive frames video processing was implemented for a real-time face recognition system. About 128 images were used for tra ining and 160 images were tested and it achieves an accuracy of 96.8% in rea l -time testing

Towards real-time visual biometric authentication using human face for healthcare telepresence mobile robots

Telepresence Mobile Robots have prominent attributes in many fields as it provides virtual presence for human robot interaction. The deployment of this robot in healthcare sector has improved patient care and health. The vision system in a telepresence robot allows two way audiovisual communication between people at different location. In spite of such advancement, the manual way of controlling a robot to recognise and track people during an emergency is not favourable for a long duration. To circumvent this problem, biometric method using human face is proposed in this research which is implemented on Medical Telediagnosis Robot. This paper details the design of the face recognition and tracking system with four automated modules which are motion detection, face detection, face recognition and face tracking. The modules are developed with different algorithm and tested individually to ensure the stability of the system. Artificial Intelligence technique was applied at the face recognition stage while a two degree of freedom mechanism for actuator control was used at face tracking stage. A sequential mode operation is proposed to reduce the execution time in a real-time environment. To achieve this, only one module is operated at each time. A Graphical User Interface was developed to ease the users at the local and robot environment. The system is designed in LabVIEW platform. The biometric system proposed with hybrid algorithm at each module adapts for face images detected at different distances, poses and lighting condition. This system was tested in real-time and has an execution time of 55ms and 98% accuracy. The stand alone system designed for Medical Telediagnosis Robot can be will be very fruitful for various biometric system using facial technology

Genetic algorithm fine tuning of Support Vector Data Descriptor (SVDD) for classification of monocotyledon and dicotyledon weeds

Weed recognition using image processing has been performed and improved in various papers. In this paper, weed seedlings were discriminated using Support Vector Data Descriptor (SVDD) to identify monocotyledon weeds from mixture of monocotyledon and dicotyledon weeds. The feature selection and parameter fine tuning were performed using genetic Algorithm (GA). The resulting SVDD configurations were tested using 200 image samples. The best configurations gave an average of 95% recognition rate