A novel fuzzy rule base system for pose independent faces detection

Moallem, Payman; Mousavi, B. Somayeh; Monadjemi, S. Amirhassan

doi:10.1016/j.asoc.2010.05.024

Cited by 30 publications

(16 citation statements)

References 6 publications

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“…Moallem et al [37] proposed Fuzzy Inference Systems (FIS) for skin segmentation based on Euclidean distance, Fuzzy rules, and genetic algorithms (GA). They used more than 1,000,000 pixels gathered from skin samples of different face databases.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Skin color modeling for face detection and segmentation: a review and a new approach

Chen

Wang

Jiang

et al. 2014

Multimed Tools Appl

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Detecting skin-colored pixels, although seems a straightforward easy task, has proven to be quite a challenging task in images that are captured under complex unconstrained imaging conditions. Color segmentation is an important method of segmenting the body in images. In this paper, we first provides a review of skin color modeling research works for face detection. In particular, we focus on discussing the challenges of skin color modeling, detection, and segmentation, and then we present the skin color modeling and region-based skin segmentation. We also propose a new approach to analyze the hue characteristics of monitoring personnel in coal mine. We determine the skin color boundaries of the miners using the boundary detection method based on the response of visual hue. And then, we apply to determining hue area method using dual hue detector to segment skin color for miners. Experiments show that the method has excellent ability to segment skin regions of miners. We also summarize the most widely used methods for skin detection approaches and collecting their numerical evaluation results.

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Section: Fuzzy Logicmentioning

confidence: 99%

Skin color modeling for face detection and segmentation: a review and a new approach

Chen

Wang

Jiang

et al. 2014

Multimed Tools Appl

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“…Skin color information is the most popular feature that employed for face localization and tracking [3][4][5][6]. Motion analysis is another method for face detection [7,8].…”

Section: Introductionmentioning

confidence: 99%

Camera-based eye blinks pattern detection for intelligent mouse

Fathi

Abdali-Mohammadi

2014

SIViP

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Human-computer interface systems provide an alternative input modality to allow people with severe disabilities to access computer systems. One of the inexpensive and unobtrusive methods for this purpose is image-based eye blinks detection. Currently, available human-computer interface systems are often intrusive, limit in head rotation, require special hardware, and have special lighting or manual initialization. This paper presented a new robust method for real-time eye blinks detection. This method enables interaction using "blink patterns," which are sequences of long and short blinks interpreted as semiotic messages. The precise location of the eye is determined automatically through multi-cues, accompanied by integration of eye variance feature and Gaussian Mixture Model classifier. The detected eye window is converted into a binary image. The eyelid's distance is extracted by applying a variance projection derivative function. By following the eyelid's distance in a finite-state machine, the blink patterns can be detected. The performance of the presented algorithm is evaluated using several frame streams. The experimental results show a robust eye blink pattern detection system in real environments.

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“…In first stage to locate face area in an input image a robust pose independent algorithm is applied which has a priority of estimating face poses beside locate them in color images [7]. To design more efficient system, after detecting face area, it will be classifies as frontal, near frontal or profile.…”

Section: Face Detection Systemmentioning

confidence: 99%

“…In this paper both psychologcal and soft computing sciences are applied together to design, novel, robust, automatic system to diagnosis mental illness, based on the face features. To this aim, in first step, an accurate system is applied to detect face regions in an input image [7]. After this, a fuzzy inference system (FIS) is designed according to special face features of patient's face.…”

Section: Introductionmentioning

confidence: 99%

Designing Fuzzy Inference System to Diagnosis down syndrome by Face Processing

Keikhayfarzaneh¹,

Mousavi²,

Khalatbari³

2011

IJCA

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Various kind of mental illness is common in all parts of the globe, and all age groups. New diagnostic methods will be vital stage in reducing or eliminating these types of disease. In this paper novel fuzzy rule-base system is designed, and programmed by computer software, for diagnosis of Down Syndrom faces in an image. System input is an arbitrary color image. In first step face regions should be selected. An accurate face detection system is utilized which applies skin color, lip position, face shape information and ear texture properties, as the key parameters. After this stage, detected face regions are processing carefully by proposed fuzzy system, and some features such as face area and eye distance are investigating carefully. Finally the probability of being Down Syndrom is revealed by designed system. 98.33% correct detection is obtained applying this algorithm on various image databases. This system could be considered as the first step to device automatic system for diagnosis of illness and would help psychiatry.

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A novel fuzzy rule base system for pose independent faces detection

Cited by 30 publications

References 6 publications

Skin color modeling for face detection and segmentation: a review and a new approach

Skin color modeling for face detection and segmentation: a review and a new approach

Camera-based eye blinks pattern detection for intelligent mouse

Designing Fuzzy Inference System to Diagnosis down syndrome by Face Processing

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