A human face detection method based on connected component analysis

Borah, Sagarika; Konwar, Sharmila; Tuithung, Themrichon; Rathi, Rahul

doi:10.1109/iccsp.2014.6950033

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…Specifically, Fig. 6 presents the normalized results for B(1) , B(10),B (14), B (25),and B (31). The following conclusions were obtained: 1) In both Fig.…”

Section: B Depth Estimation From Tracked Pointsmentioning

confidence: 74%

“…The lack of computing resources with which to analyze the dynamics of visual technology has resulted in both generative and discriminative models [28]- [30]. A generative model identifies similar regions in the target image area, while a discriminative mechanism uses background differentiation and assigns binary numbers to the target object, which are later analyzed using a local orientation histogram and pixel colors [31]. A tracking-modeling-detection (TMD) mechanism is used for tracking the objects that train the classifiers [28], [32].…”

Section: Related Workmentioning

confidence: 99%

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Detection and Tracking of Faces in 3D Using a Stereo Camera Arrangements

AlQahtani¹,

Banks²,

Chandran³

et al. 2019

IJMLC

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3D facial tracking has become vital to the continued integration of computers, technology, and human society. In recent decades, the integration of technology has increased, and the use of surveillance, conference calls, gaming components, and other similar applications has spurred demand for the ability to recognize the distinctive features of humans. However, in order for these new technologies to function effectively and reach their fullest potential, a great deal of work is still needed. The field of facial mapping and tracking is still in its early developmental stages, necessitating additional research into the best methods of tracking and monitoring specific human faces. To this end, an algorithm has been created that would allow for improvements in this area; however, a video was first required that could be used effectively for the algorithm. Two web cameras running on Raspberry Pi were used to gather the footage necessary for detecting and tracking specific facial features. While certain limitations were identified throughout the process, the algorithm still achieved significant successful tracking results. In spite of this success, further efforts are still needed to effectively explore the proposed algorithm and improve upon these initial results.

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“…Specifically, Fig. 6 presents the normalized results for B(1) , B(10),B (14), B (25),and B (31). The following conclusions were obtained: 1) In both Fig.…”

Section: B Depth Estimation From Tracked Pointsmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

Detection and Tracking of Faces in 3D Using a Stereo Camera Arrangements

AlQahtani¹,

Banks²,

Chandran³

et al. 2019

IJMLC

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show abstract

“…The methodology for identifying the face area utilizing skin color and the Maximum Morphological Gradient Combination image was exhibited [3][4]. The system failed when it manages with skin color areas including similar color background and region of dress.…”

Section: Survey Workmentioning

confidence: 99%

Detection of Human Faces in Video Sequences Using Mean of GLBP Signatures

Selvi¹,

Rani²,

Pradahnji³

2020

IFIP Advances in Information and Communication Technology

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Machine analysis of detection of the face is robust research topic in humanmachineinteraction today. The existing studies reveal that discovering the position and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. We designed work is spontaneous and vigorous method to identify the location of face area using recently developed You Tube Video face database. Formulate the normalization technique in each frame. The frame is separated into overlapping regions. The Gabor signatures extracted on each region by Gabor filters with different scale and orientations. The Gabor signatures are averaged and then local binary pattern histogram signatures are extracted. The Gabor local binary pattern signatures are passed to Gentle Boost categorizer with the assistance of face and non-face signature of the gallery images for identifying the portion of the face region. Our experimental results on YouTube video face database exhibits promising results and demonstrate a significant performance improvement when compared to the existing techniques. Furthermore, our designed work is uncaring to head poses and sturdy to variations in illumination, appearance and noisy images.

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“…Here, we have reviewed some of the papers related to our work. The methodology for identifying the face area utilizing skin color and the Maximum Morphological Gradient Combination image was exhibited [3][4]. The system failed when it manages with skin color areas including similar color background and region of dress.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of Human Faces in Video Sequences using NGLBP Feature

2019

IJRTE

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Machine analysis of face detection is an interesting topic for study in Human-Computer Interaction. The existing studies show that discovering the position and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. This paper suggests a method to detect the location of face area using recently developed YouTube Video face database. In this work, each frame is formulated by normalization technique and separated into overlapping blocks. The Gabor filter is tuned to extract the Gabor features from the individual blocks. The averaged Gabor features are manipulated and local binary pattern histogram features are extracted. The extracted patterns are passed to the classifier with training images for face region identification. Our experimental results on YouTube video face database exhibits promising results and demonstrate a significant performance improvement when compared to the existing techniques. Furthermore, our proposed work is uncaring to head poses and sturdy to variations in illumination, appearance and noisy images

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A human face detection method based on connected component analysis

Cited by 8 publications

References 5 publications

Detection and Tracking of Faces in 3D Using a Stereo Camera Arrangements

Detection and Tracking of Faces in 3D Using a Stereo Camera Arrangements

Detection of Human Faces in Video Sequences Using Mean of GLBP Signatures

Detection of Human Faces in Video Sequences using NGLBP Feature

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