Real-time iris detection on faces with coronal axis rotation.
ABSTRACT Real-time face and iris detection on video sequences is important in diverse applications such as, study of the eye function, drowsiness detection, virtual keyboard interfaces, face recognition and multimedia retrieval. In previous work we developed a non-invasive real time iris detection method consisting of three stages: coarse face detection, fine face detection and iris boundary detection. In this paper, iris detection is considered on faces with rotations in the coronal axis within the range -40° to 40°. It is shown that a line integral over the directional image as a function of the template rotation, has a maximum when the face and template coincide in rotation angle. The method was applied on 10 video sequences, with a total of 6470 frames, from different subjects rotating their faces in the coronal axis. Results of correct face detection on 8 video sequences were 100%, one reached 99.9% and one 98.2%. Results on correct iris detection are above 96% in 9 of the video sequences and one reached 77.8%. The method was implemented in real-time (30 frames per second) with a PC 1.8 GHz.
Conference Paper: A Drowsy Driver Detection and security system[Show abstract] [Hide abstract]
ABSTRACT: This paper presents a new approach towards automobile safety and security. We propose three distinct but closely related concepts viz. an Iris Recognition system, a Drowsy Driver Detection system and a Distress Signalling system using non-intrusive machine vision based concepts. In recent time's automobile theft and fatigue related crashes have really magnified. In order to minimize these issues, we have incorporated Biometric security through Iris recognition that will help in authentication and an improved sleep detection and driver alert system by monitoring both the driver's eyes as well as senseing the heat variation of the body via infrared thermal sensor. Distress Signalling system is incorporated for drivers to get assistance from the Police in need without revealing it to people present around him. This paper combines computer vision, pattern recognition and optics. All image processing was performed using NI Vision Assistant. Also NI LabVIEW was used to take the current body temperature from the temperature sensors attached to the DAQ (Data Acquisition) Signal Accessory.Ultra Modern Telecommunications & Workshops, 2009. ICUMT '09. International Conference on; 11/2009
Conference Paper: Drivers drowsiness detection in embedded system[Show abstract] [Hide abstract]
ABSTRACT: It is a difficult problem to make drivers drowsiness detection meet the needs of real time in embedded system; meanwhile, there are still some unsolved problems like drivers' head tilted and size of eye image not large enough. This paper proposes an efficient method to solve these problems for eye state identification of drivers' drowsiness detection in embedded system which based on image processing techniques. This method break traditional way of drowsiness detection to make it real time, it utilizes face detection and eye detection to initialize the location of driver's eyes; after that an object tracking method is used to keep track of the eyes; finally, we can identify drowsiness state of driver with PERCLOS by identified eye state. Experiment results show that it makes good agreement with analysis.Vehicular Electronics and Safety, 2007. ICVES. IEEE International Conference on; 01/2008
- [Show abstract] [Hide abstract]
ABSTRACT: Real-time eye and iris tracking is important for handsoff gaze-based password entry, instrument control by paraplegic patients, Internet user studies, as well as homeland security applications. In this project, a smart camera, LabVIEW and vision software tools are utilized to generate eye detection and tracking algorithms. The algorithms are uploaded to the smart camera for on-board image processing. Eye detection refers to finding eye features in a single frame. Eye tracking is achieved by detecting the same eye features across multiple image frames and correlating them to a particular eye. The algorithms are tested for eye detection and tracking under different conditions including different angles of the face, head motion speed, and eye occlusions to determine their usability for the proposed applications. This paper presents the implemented algorithms and performance results of these algorithms on the smart camera.01/2011;