Facial feature detection using geometrical face model: An efficient approach

Jeng, Shi-Hong; Liao, H.-Y.M.; Han, Chin Chuan; Chern, Ming Yang; Liu, Yao Tsorng

doi:10.1016/s0031-3203(97)00048-4

Cited by 123 publications

(53 citation statements)

References 7 publications

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“…Simply, the basic idea of this kind of method is to capture the relative position and relative size of representative facial components, such as eyebrows, eyes, nose, and mouth [52]. Then face contour information is included to classify and recognize the faces.…”

Section: Face Recognitionmentioning

confidence: 99%

A Software Framework for PCa-Based Face Recognition

Peng

Alencar

Cowan

2016

2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE)

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Section: Face Recognitionmentioning

confidence: 99%

A Software Framework for PCa-Based Face Recognition

Peng

Alencar

Cowan

2016

2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE)

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“…Applications in law enforcement for mug shot identification, verification for personal identification such as driver's licenses and credit cards, gateways to limited access areas, and surveillance of crowd behaviour are all potential applications of a successful face recognition system. Face recognition has become an important issue in many applications such as security systems, credit card verification and criminal identification [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Principal Component Analysis and Linear Discriminant Analysis

nka¹

2015

IJAREEIE

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This paper presents the two novel face detection techniques which are based on the singular vector decomposition (SVD) and Eigen value decomposition (EVD). Here, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods are applied to detect the features of faces which act as the principle component for the face recognition problem. The human face is full of information but working with all the information is time consuming and less efficient. It is better get unique and important information and discards other useless information in order to make system efficient. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigen faces are calculated from the initial face image set. New faces are projected onto the space expanded by eigen faces and represented by weighted sum of the eigen faces. These weights are used to identify the faces. To reduce the time complexity and Euclidean distance in face space, here two techniques Singular Value Decomposition and Eigen-value decomposition are utilized. Simulation results have been presented to illustrates the effectiveness of the proposed face detection techniques. Here, face detection is performed using Principal Component Analysis and Linear Discriminant Analysis methods with Singular Value and Eigen-value decomposition. Experiments on face database shows the effectiveness of our proposed algorithm and results compared to PCA using Eigen Value Decomposition(EVD) shows that the proposed scheme gives comparatively better results than previous methods in terms of reduced time complexity without effecting its accuracy. Based on the results presented it is concluded that the Principal Component Analysis using EVD leads to superior results compared to that of other reported methods. From the result it is also noticed that the time complexity of the proposed method reduces considerable which leads to real time applicability of the proposed method.

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“…Jeng et al [11] proposed a template matching algorithm using a geometrical face model using relative distance be- Finally, each candidate region is classified as a face and nonface region by matching the constructed depth map based block rank patterns and a template that is generated from training data set. For template matching, the 5×5 template block rank pattern is a priori constructed by averaging block ranks of training data set.…”

Section: Introductionmentioning

confidence: 99%

“…Jeng et al [11] proposed a template matching algorithm using a geometrical face model using relative distance between facial features. Hiremath and Danti [12] improved Jeng et al's algorithm using additional color information.…”

Section: Introductionmentioning

confidence: 99%

“…To construct a template of accurate face region, Kherchaoui and Houacine [8] proposed a template matching algorithm using a model of skin color with constraints. Template matching algorithms using skin information and lines of face template was also presented [9].Liu et al [10] proposed a template matching algorithm using radial template to detect rotated face in any orientation.Jeng et al [11] proposed a template matching algorithm using a geometrical face model using relative distance be- Finally, each candidate region is classified as a face and nonface region by matching the constructed depth map based block rank patterns and a template that is generated from training data set. For template matching, the 5×5 template block rank pattern is a priori constructed by averaging block ranks of training data set.…”

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confidence: 99%

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Face Detection Using Adaboost and Template Matching of Depth Map based Block Rank Patterns

Kim¹,

Park²,

Mun³

2012

Journal of Broadcast Engineering

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성능을 Kinect sensor로 취득한 실제 영상으로 실험하였다. 실험 결과 true positive를 잘 보존하면서 많은 false positive들을 효과적으 로 제거하는 것을 보여준다. AbstractA face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into 5×5 blocks and depth values are averaged in each block. Then, 5×5 block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the 5×5 template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved. Other face detection approach is template matching, in which the template (constructed from training set) and a pattern of candidate region (detected from input image) are compared to classify face and nonface. Various algorithms using template matching have been studied. Predefined fixed-size windows were used as templates to detect facial features [6]. Then, the template matching algorithm using variable size window that can detect faces of varying sizes was presented [7]. To construct a template of accurate face region, Kherchaoui and Houacine [8] proposed a template matching algorithm using a model of skin color with constraints. Template matching algorithms using skin information and lines of face template was also presented [9].Liu et al. [10] proposed a template matching algorithm using radial template to detect rotated face in any orientation.Jeng et al. [11] proposed a template matching algorithm using a geometrical face model using relative distance be- Finally, each candidate region is classified as a face and nonface region by matching the constructed depth map based block rank patterns and a template that is generated from training data set. For template matching, the 5×5 template block rank pattern is a priori constructed by averaging block ranks of training data set. To obtain training and test data set, we use Kinect range sensor [21]. Infrared projector projects infrared patterns that people cannot see.Infrared image is obtained by infrared camera and used to generate depth map. However, the regions that are not projected by occlusion have no depth data.The rest of the paper is structured as follows. Sec...

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Facial feature detection using geometrical face model: An efficient approach

Cited by 123 publications

References 7 publications

A Software Framework for PCa-Based Face Recognition

A Software Framework for PCa-Based Face Recognition

Face Recognition Based on Principal Component Analysis and Linear Discriminant Analysis

Face Detection Using Adaboost and Template Matching of Depth Map based Block Rank Patterns

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