Real-time face location on gray-scale static images

In this paper, a robust method is developed to locate the irises of both eyes. The method doesn't put any restrictions on the background. The method is based on the AdaBoost algorithm for face and eye candidate points detection. Candidate points are tuned such that two candidate points are exactly in the centers of the irises. Mean crossing function and convolution template are proposed to filter out candidate points and select the iris pair. The advantage of using this kind of hybrid method is that AdaBoost is robust to different illumination conditions and backgrounds. The tuning step improves the precision of iris localization while the convolution filter and mean crossing function reliably filter out candidate points and select the iris pair. The proposed structure is evaluated on three public databases, Bern, Yale and BioID. Extensive experimental results verified the robustness and accuracy of the proposed method. Using the Bern database, the performance of the proposed algorithm is also compared with some of the existing methods.

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“…Firstly eyes possess strong horizontal edges [6]. Secondly, the pupil appears darker than its surrounding.…”

Section: Previous Workmentioning

confidence: 99%

A Robust Approach to Automatic Iris Localization

Ali

Kim

2009

Journal of the Optical Society of Korea

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“…In Table 2 some tests with different videos are reported using another approach of head tracking [12]. This developed method integrates and improves the ideas proposed in [5] and [27]. The first searches for head template with both colour and gradient feature but the search space is limited to a neighbourhood of a predicted position.…”

Section: Surveillance and Biometric Sys-temsmentioning

confidence: 99%

“…The problem of this solution is that it needs a frame rate too high to make reliable predictions. Instead, [27] adopts a solution based on the elliptical Hough transform; it works at each frame and does not require any tracking nor prediction. A face colour histogram must be available as a model.…”

Section: Surveillance and Biometric Sys-temsmentioning

confidence: 99%

Multimedia surveillance systems

Cucchiara

2005

Proceedings of the Third ACM International Workshop on Video Surveillance &Amp; Sensor Networks

134

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The integration of video technology and sensor networks constitutes the fundamental infrastructure for new generations of multimedia surveillance systems, where many different media streams (audio, video, images, textual data, sensor signals) will concur to provide an automatic analysis of the controlled environment and a real-time interpretation of the scene. New solutions can be devised to enlarge the view of traditional surveillance systems by means of distributed architectures with fixed and active cameras, to enhance their view with other sensed data, to explore multi-resolution views with zooming and omnidirectional cameras. Applications regard surveillance of wide indoor and outdoor area and particularly people surveillance: in this case, multimedia surveillance systems can be enriched with biometric technology; the best views of detected persons and their extracted visual features (e.g. faces, voices, trajectories) can be exploited for people identification.VSSN05 is the third edition of the workshop, co-located at ACM Multimedia Conference, that embraces research reports on video surveillance and, since the edition of 2004, sensor networks. This paper gives a short overview of the hot topics in multimedia surveillance systems and introduces some research activities currently engaged in the world and presented at VSSN05.

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“…It is an exhaustive search of the input image for possible face locations at all scales. To reduce search time, we apply the Maio's approximate location [4]. A directional image is a matrix defined over a discrete grid, superimposed on the gray-scale image, whose elements are in correspondence with the grid nodes.…”

Section: Face Locationmentioning

confidence: 99%

“…It means that face verification network was applied to input image for possible face locations at all scales. For efficient face location strategy, Maio and Maltoni suggested real-time face location algorithm based on the search of elliptical blobs in the directional image by means of the generalized Hough transform [4]. In this paper, we used Maio's approach to find the face candidate regions.…”

Section: Introductionmentioning

confidence: 99%

Face detection using quantum-inspired evolutionary algorithm

Jang

Han

Kim

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

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Abstract-This paper proposes a new face detection system using Quantum-inspired Evolutionary Algorithm (QEA). The proposed detection system is based on elliptical blobs and Principal Components Analysis (PCA). The elliptical blobs in the directional image are used to find the face candidate regions, and then PCA and QEA are employed to verify faces. Although PCA related algorithms have shown outstanding performance, there still exist some problems such as optimal decision boundary or learning capabilities. By PCA, we can obtain the optimal basis but they may not be the optimal ones for discriminating faces from non-faces. Moreover, a threshold value should be selected properly considering the success rate and false alarm rate. To solve these problems, QEA is employed to find out the optimal decision boundary under the predetermined threshold value which distinguishes between face images and non-face images. The proposed system provides learning capability by reconstructing the training database, which means that system performance can be improved as failure trials occur.

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Real-time face location on gray-scale static images

Cited by 109 publications

References 34 publications

A Robust Approach to Automatic Iris Localization

A Robust Approach to Automatic Iris Localization

Multimedia surveillance systems

Face detection using quantum-inspired evolutionary algorithm

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