A computational model for face location

Govindaraju, Venu; Srihari, Sargur N.; Sher, David B.

doi:10.1109/iccv.1990.139626

Cited by 72 publications

(21 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Far from modeling the HUB, Govindaraju et al [7][8][9][10] has detected head-and-shoulder based on the geometry properties of face profile. Prior information was required and their system has a high error rate.…”

Section: Research Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Modeling of Human Upper Body for Sign Language Recognition

Bilal

Akmeliawati

Shafie

et al. 2011

The 5th International Conference on Automation, Robotics and Applications

View full text Add to dashboard Cite

Abstract-Sign Language Recognition systems require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. Head, face, forehead, shoulders and chest are very crucial parts that can carry a lot of positioning information of hand gestures in gesture classification. In this paper as the main contribution, a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. Scaling the extracted parts during body orientation was attained using partial estimation of face size. Tracking the extracted parts for front and side view was achieved using CAMSHIFT [24]. The outcome of the system makes it applicable for real-time applications such as Sign Languages Recognition (SLR) systems.

show abstract

Section: Research Backgroundmentioning

confidence: 99%

“…Many existing methods which use geometric modeling, boosted classifiers and SVM have been introduced [7][8][9][10][11][12]. But real-time applications, such as SL recognition systems, require a fast, compatible and synchronized method for HUB parts detection and tracking.…”

Section: Modeling the Human Upper Bodymentioning

confidence: 99%

Modeling of Human Upper Body for Sign Language Recognition

Bilal

Akmeliawati

Shafie

et al. 2011

The 5th International Conference on Automation, Robotics and Applications

View full text Add to dashboard Cite

show abstract

“…The objective function Fhead(xe, ye) used to evaluate the parameter set generated by genetic string for this search is as follows: (3) where (4) and le is the contour length of the ellipse, 9 is a smoothed version of edge map 12, and h(x, y) is pixel intensity at location (x, y).…”

Section: Head Localizationmentioning

confidence: 99%

Locating and Extracting Human Face in a Cluttered Scene by using Genetic Search

et al. 1998

View full text Add to dashboard Cite

This work investigates a new approach to detect human face in a cluttered scene. Our method consists of two main search procedures, both using genetic algorithm. The first one is to locate a head inside the scene and the second one is to identify the existence of face within the extracted head area. For this purpose, we have developed two models to he used as aa tool to calculate the fitness for each observation in the search procedure: One is a head model which is approached by an ellipse and the other one is a face template which size is adjustable. To find a face, we exploited face salient features such as eyes and nose/mouth on binary image where the features look darker. The procedures work sequentially. The head search is activated first, and after the head area is found, the face detection is activated. The experiment demonstrates the effectiveness of the method. Since our ultimate aim is to provide a good quality of face image for recognition, we also made comparison between face image magnification by linear interpolation with that obtained by zooming camera. The results gives our preference toward that, obtained by zooming camera.

show abstract

“…The outline of a human face can be approximated by an ellipse [10], or more precisely, by connected arcs [8]. Furthermore, typical face outlines have been found to have aspect ratios in a narrow range between 1.4-1.6, and tilt in the range ( 30 , 30 ) [8].…”

Section: A Shape Constraints On Human Facesmentioning

confidence: 99%

“…For consistency, we use the phrase face detection in this paper. Govindaraju et al [8] proposed a model-based approach where the face is defined as interconnected arcs that represent chins and hairline. The arcs are extracted using low-level computer vision algorithms and are then grouped based on cost minimization to detect candidate face regions.…”

mentioning

confidence: 99%

A highly efficient system for automatic face region detection in MPEG video

Wang

Chang

1997

IEEE Trans. Circuits Syst. Video Technol.

239

View full text Add to dashboard Cite

Abstract-Human faces provide a useful cue in indexing video content. In this paper, we present a highly efficient system that can rapidly detect human face regions in MPEG video sequences. The underlying algorithm takes the inverse quantized discrete cosine transform (DCT) coefficients of MPEG video as the input, and outputs the locations of the detected face regions. The algorithm consists of three stages, where chrominance, shape, and frequency information are used, respectively. By detecting faces directly in the compressed domain, there is no need to carry out the inverse DCT transform, so that the algorithm can run faster than the real time. In our experiments, the algorithm detected 85-92% of the faces in three test sets, including both intraframe and interframe coded image frames from news video. The average run time ranges from 13-33 ms per frame. The algorithm can be applied to JPEG unconstrained images or motion JPEG video as well.Index Terms-Compressed-domain approach, face detection, MPEG video, video indexing.

show abstract

A computational model for face location

Cited by 72 publications

References 3 publications

Modeling of Human Upper Body for Sign Language Recognition

Modeling of Human Upper Body for Sign Language Recognition

Locating and Extracting Human Face in a Cluttered Scene by using Genetic Search

A highly efficient system for automatic face region detection in MPEG video

Contact Info

Product

Resources

About