Rapid stereo-vision enhanced face detection

Kosov, Sergey; Scherbaum, Kristina; Faber, Kamil; Thormählen, Thorsten; Seidel, Hans‐Peter

doi:10.1109/icip.2009.5413675

Cited by 11 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…4(b) is to be matched with 5×5 template block rank pattern   . [16], 우리의 이전 연구 알고리즘 [20], 제안하는 알고리즘의 성능 비교 …”

Section: Construction Of Block Rank Patternunclassified

“…첫 번째 열은 입력 영상. 나머지 열은 왼쪽부터 Kosov 등이 제안한 알고리즘 [16], 우리의 이전 연구 알고리즘 [20], 제안하는 알고리즘에 의해 생성되는 결과 영상 show that the proposed algorithm is more robust to complex background than our previous algorithm because of employing expanded block rank pattern that contains background around a face.…”

Section: Construction Of Block Rank Patternunclassified

“…This paper proposes a face detection algorithm using a depth map based 5×5 block rank pattern that is robust to complex background or varying sizes of face. This paper is an extended version of our previous algorithm [20], in which modification of block rank pattern and performance comparison with conventional algorithm using PCA in depth map [16] The rest of the paper is structured as follows. Section Ⅱ presents the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Nilsson et al [15] information (i.e., depth image that represents the distance between a camera and objects) is also used to reliably perform face detection. Kosov et al [16] proposed a depth map based face detection algorithm using principal component analysis (PCA), where the depth map is generated from captured stereo images. To reduce search region, Wu et al [17] used constraints that the distance between a camera and face is limited.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kim¹,

Park²,

Mun³

2012

Journal of Broadcast Engineering

View full text Add to dashboard Cite

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...

show abstract

“…4(b) is to be matched with 5×5 template block rank pattern   . [16], 우리의 이전 연구 알고리즘 [20], 제안하는 알고리즘의 성능 비교 …”

Section: Construction Of Block Rank Patternunclassified

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Kim¹,

Park²,

Mun³

2012

Journal of Broadcast Engineering

View full text Add to dashboard Cite

show abstract

“…To find the minimum, we use a real-time variational approach [10]. The faces are detected with a real-time stereo-enhanced face detection algorithm [11]. For each detected face we extract the face region and the corresponding disparity map.…”

Section: Disparity Estimation and Face Detectionmentioning

confidence: 99%

Rapid stereo-vision enhanced face recognition

Kosov

Thormählen

Seidel

2010

2010 IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

This paper presents a real-time face recognition system. The system uses a stereo camera to locate, track, and recognize a person's face. Our algorithm improves state-of-the-art monocular 2D object recognition techniques by additionally considering the facial 3D surface, which is relatively stable under different lighting conditions. First, faces are detected and their surfaces are reconstructed from the stereo images. Afterwards, a 3D face is composed by joining 2D image data and appropriate depth data. The 3D face is then decomposed into its principal components. The principal components are used to recognize a 3D face by comparing characteristics of the current face to those of known individuals in a database. The result is an efficient and accurate face recognition algorithm. To evaluate our approach, we compared its performance to a classical monocular face recognition algorithm and observed that the recognition rate increased on average by 7.7 percent.

show abstract

Performance analysis of Gabor wavelet for extracting most informative and efficient features

Malathi

Bhuyan

2016

Multimed Tools Appl

View full text Add to dashboard Cite

Rapid stereo-vision enhanced face detection

Cited by 11 publications

References 10 publications

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

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

Rapid stereo-vision enhanced face recognition

Performance analysis of Gabor wavelet for extracting most informative and efficient features

Contact Info

Product

Resources

About