Real-time iris detection on faces with coronal axis rotation
Dept. of Electr. Eng., Chile Univ., Santiago, Chile
DOI: 10.1109/ICSMC.2004.1401404 Conference: Proceedings of the IEEE International Conference on Systems, Man & Cybernetics: The Hague, Netherlands, 10-13 October 2004
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.
Available from: Linh Manh Pham
- "Many existing systems require PCs for vision processing , . However, the PC is not adequate to withstand environmental factors in the actual field. "
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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.
Available from: Pablo Estevez
- "A ratio between two line integrals over a semicircular template is developed to detect the iris–sclera boundary. Preliminary results for different parts of the proposed method were previously presented in , , and . "
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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 this paper, a real-time robust method is developed to detect irises on faces with coronal axis rotation within the normal range of to . The method allows head movements with no restrictions to the background. The method is based on anthropometric templates applied to detect the face and eyes. The templates use key features of the face such as the elliptical shape, and location of the eyebrows, nose, and lips. For iris detection, a template following the iris-sclera boundary shape is used. The method was compared to Maio-Maltoni's and Rowley's methods for face detection on five video sequences (TEST 1). The method was also assessed in an additional set of five video sequences for iris detection (TEST 2). Results of correct face detection in TEST 1 were above 99% in three of the five video sequences. The fourth video sequence reached 97.6% and the third 90.6%. In TEST 2, the iris detection was above 96% in all five video sequences with two above 99.7% and two at 100%. Face size estimation is also above 99.9%. The average processing time of our method was 0.02 s per frame. Thus, the proposed method can process frames at a rate near to 50 frames/s, and therefore, is applicable in real time in a standard personal computer (PC 1.8 GHz).
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ABSTRACT: Real-time face detection on video sequences is important in diverse
applications such as, man-machine interfaces, face recognition, security
and multimedia retrieval. In this work, we present a new method based on
the maximization of local components in the directional image to
optimize templates for frontal face detection. In the past, several
methods for face detection have been developed using face templates.
These templates are based on common face features such as eyebrows,
eyes, nose and mouth. Templates have been applied to a directional image
containing faces computing a line integral to detect faces with high
accuracy. In this paper, the maximization of local components in the
directional image is used to select new templates optimizing its size
and response to a face in the directional image. The method selects
common directional vectors in a set of frontal faces to generate the
template. The method was tested on 386 images from the Caltech face
database and 55 images from the Purdue database. Results were compared
to those of the traditional anthropometric templates that contain
features from the eyebrow, nose and mouth. Results show that the new
templates have significant better performance in the estimation of face
size and the line integral value. Face detection reached 97% on the
Caltech face database and 98% on the Purdue database. The templates have
fewer number of points compared to the traditional anthropometric
templates which will lead to lower processing time.
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