Eye detection based on the Viola-Jones method and corners points

Kaddouhi, Samir El; Saaidi, Abderrahim; Abarkan, Mustapha

doi:10.1007/s11042-017-4415-5

Cited by 23 publications

(9 citation statements)

References 37 publications

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“…Once the eye area has been detected, iris area will be detected. Previous studies have examined on various subjects of different races and found that the iris is amongst the darkest circular region of the eye where has been characterized by its lower intensity compared with the surrounding region [39] [40]. In this research, the potential irises will be detected using Circular Hough Transform (CHT) due to its robustness in the presence of noise and varying illumination.…”

Section: A Eye Area Detectionmentioning

confidence: 99%

Face Recognition for Varying Illumination and Different Optical Zoom Using a Combination of Binary and Geometric Features

et al. 2020

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Preservation of image features caused by binary conversion is a difficult task under variation of illumination conditions. Several binary conversion-based methods have used an adaptive thresholding technique to improve their performance under illumination variation conditions because of its robustness. However, the performances of existing methods were still limited under high differences illumination conditions especially for uncontrolled lighting sources. In addition, various length of face-to-camera distance gives significant problem affect for the performance of face recognition method. It happens when various images are available for the same person with different length face-to-camera distances due to the appearance of varying facial features of the same person. Therefore, this study proposed to combine the strength normalization and feature-based method to build an illumination distribution model to overcome this problem. With the proposed method the illumination model will fit with variation of illumination conditions in a whole image to generate an adaptive threshold for a novel columnar binary conversion method. The proposed method consists of five main stages, starting with eye area detection using the developed Viola-Jones algorithm. Next, the iris is detected using the Circular Hough Transform (CHT) method and will convert it into binary using the proposed Columnar Binary Conversion (CBC) method to preserve the appearance of the facial features under the illumination variation. Then, the proposed Facial Feature Region Normalization (FFRN) method is performed to improve the effects of different optical zooms for the classification step. The classification is conducted based on the similarity measurement between the extracted normalised binary face region and the dataset that must be converted into their equivalent normalised binary images. The proposed method is evaluated on two different smartphone databases, namely as Smartphone Face Video (SFV) and MOBIO. The performance results showed the outperformance of the proposed method.

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Section: A Eye Area Detectionmentioning

confidence: 99%

Face Recognition for Varying Illumination and Different Optical Zoom Using a Combination of Binary and Geometric Features

et al. 2020

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“…By using multiple feature extractors, benefits of each feature type improves overall performance. Here [13] faces are detected using viola and jones method then a Shi-Tomasi detector finds potential corner points of eyes and lastly k-means allows clustering of neighbor corner points to determine eye regions. Infrared imaging takes advantage of the generally constant distribution of face temperatures to achieve more reliable detection.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Face Detection Based on Haar-Like and MB-LBP Features

Dandashy¹,

Hassan²,

Bitar³

2019

IJEMR

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The effective real-time face detection framework proposed by Viola and Jones gained much popularity due its computational efficiency and its simplicity. A notable variant replaces the original Haar-like features with MB-LBP (Multi-Block Local Binary Pattern) which are defined by the local binary pattern operator, both detector types are integrated into the OpenCV library. However, each descriptor and its evaluation method has its own set of strengths and setbacks. In this paper, an enhanced two-layer face detector composed of both Haar-like and MB-LBP features is presented. Haar-like features are employed as a coarse filter but with a new evaluation involving dual threshold. The already established MB-LBPs are arranged as the fine filter of the detector. The Gentle AdaBoost learning algorithm is deployed for the training of the proposed detector to reach the classification and performance potential. Experiments show that in the early stages of classification, Haar features with dual threshold are more discriminative than MB-LBP and original Haarlike features with respect to number of features required and computation. Benchmarking the proposed detector demonstrate overall 12% higher detection rate at 17% false alarm over using MB-LBP features singly while performing with ×3 speedup.

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“…Another popular variation is to combine the Viola-Jones method with a neural network, where the neural network is used to classify faces with non-faces processing stage (Da'san et al 2015). Additionally, the Viola-Jones method can be integrated with other known methods such as shape constraints (Cristinacce and Cootes 2003), low-level saliency (Cerf et al 2008), and with corner points using Shi-Tomasi detector (El Kaddouhi et al 2017). Recent development of the Viola-Jones detector involves implementation via graphics processing unit (GPU), multi-threaded central processing unit (CPU), and field-programmable gate array (FPGA).…”

Section: State-of-the-art Workmentioning

confidence: 99%