A Methodology for Detecting Faces from Different Views

Tsitsoulis, Athanasios; Bourbakis, Nikolaos G.

doi:10.1109/ictai.2012.40

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…Global and adaptive skin detection can be performed in almost real time, so this step is performed quickly, excluding the superpixel creation. Face detection is also fast, since the main step [33] runs in real time and significantly restricts the search space for [35]. The graph matching process described in [35] is generally computationally expensive, but considering that it is only performed on the produced regions of [33], it can be calculated quickly.…”

Section: Discussionmentioning

confidence: 99%

“…Face detection is also fast, since the main step [33] runs in real time and significantly restricts the search space for [35]. The graph matching process described in [35] is generally computationally expensive, but considering that it is only performed on the produced regions of [33], it can be calculated quickly. The most computationally demanding steps are those of mask validation during the upper and lower body extraction processes.…”

Section: Discussionmentioning

confidence: 99%

“…To refine the results of the algorithm, we propose using the skin detection method presented in [34], and the face detection algorithm presented in [35]. The skin detection method is based on color constancy and a multilayer perceptron neural network trained on images collected under various illumination conditions both indoor and outdoor, and containing skin colors of different ethnic groups.…”

Section: Face Detectionmentioning

confidence: 99%

“…Face detection here is primarily conducted using the Viola-Jones face detection algorithm for both frontal and side views. Since face detection is the cornerstone of our methodology, we refine the results of the aforementioned method using the face detection algorithm presented in [35].…”

Section: Upper Body Segmentationmentioning

confidence: 99%

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A Methodology for Extracting Standing Human Bodies From Single Images

Tsitsoulis

Bourbakis

2015

IEEE Trans. Human-Mach. Syst.

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Segmentation of human bodies in images is a challenging task that can facilitate numerous applications, like scene understanding and activity recognition. In order to cope with the highly dimensional pose space, scene complexity, and various human appearances, the majority of existing works require computationally complex training and template matching processes. We propose a bottom-up methodology for automatic extraction of human bodies from single images, in the case of almost upright poses in cluttered environments. The position, dimensions, and color of the face are used for the localization of the human body, construction of the models for the upper and lower body according to anthropometric constraints, and estimation of the skin color. Different levels of segmentation granularity are combined to extract the pose with highest potential. The segments that belong to the human body arise through the joint estimation of the foreground and background during the body part search phases, which alleviates the need for exact shape matching. The performance of our algorithm is measured using 40 images (43 persons) from the INRIA person dataset and 163 images from the "lab1" dataset, where the measured accuracies are 89.53% and 97.68%, respectively. Qualitative and quantitative experimental results demonstrate that our methodology outperforms state-of-the-art interactive and hybrid top-down/bottom-up approaches.Index Terms-Adaptive skin detection, anthropometric constraints, human body segmentation, multilevel image segmentation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Face Detectionmentioning

confidence: 99%

Section: Upper Body Segmentationmentioning

confidence: 99%

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A Methodology for Extracting Standing Human Bodies From Single Images

Tsitsoulis

Bourbakis

2015

IEEE Trans. Human-Mach. Syst.

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“…However, Viola-Jones face detector is prone to false positive detections that can lead to unnecessary activations of our algorithm and faulty hand detections. In order to refine the results of the algorithm, we propose using the skin detection method presented in [11] and face detection algorithm presented in [12]. The skin detection method is based on color constancy and a Multilayer Perceptron Neural Network (MLPNN) trained on images collected under various illumination conditions both indoor and outdoor, and containing skin colors of different ethnic groups.…”

Section: Face Detectionmentioning

confidence: 99%

Towards Automatic Hands Detection in Single Images

Tsitsoulis

Bourbakis

2013

Image Analysis and Processing – ICIAP 2013

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Detection of hands in single, unconstrained, monocular images is a very difficult task. Localization and extraction of the hand regions, however, provides important and useful knowledge that can facilitate many other tasks, such as gesture recognition, pose estimation and action recognition. In this paper we present a simple appearancebased methodology that combines face detection and anthropometric constraints to efficiently estimate the position and regions of hands in images. It requires no training neither explicit estimation of the human pose. Experimental results illustrate the performance of the approach.

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Research on Non-frontal Face Detection Method Based on Skin Color and Region Segmentation

Wang

Shen

2017

Communications in Computer and Information Science

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A Methodology for Detecting Faces from Different Views

Cited by 4 publications

References 39 publications

A Methodology for Extracting Standing Human Bodies From Single Images

A Methodology for Extracting Standing Human Bodies From Single Images

Towards Automatic Hands Detection in Single Images

Research on Non-frontal Face Detection Method Based on Skin Color and Region Segmentation

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