Incorporating skin color for improved face detection and tracking system

Dahal, Bikram; Alsadoon, Abeer; Prasad, P. W. C.; Elchouemi, A.

doi:10.1109/ssiai.2016.7459203

Cited by 12 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CamSHIFT and KLT algorithms are additional face-tracking mechanisms that have been developed in order to describe faces in a frame-by-frame manner [22]- [25]. CamSHIFT controls color distribution probability by maintaining proper sizes, and uses the meanSHIFT mechanism to locate the center of human faces [23].…”

Section: Related Workmentioning

confidence: 99%

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

AlQahtani¹,

Banks²,

Chandran³

et al. 2019

IJMLC

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

AlQahtani¹,

Banks²,

Chandran³

et al. 2019

IJMLC

View full text Add to dashboard Cite

show abstract

“…A recent progress of face detection is rotation invariant, fast speed detection, quality of the image which includes illumination, noise and blur. According to knowledge-based method, [22][23] proposed morphological technique to detect face. It is limited to accuracy of edge detection and multi face.…”

Section: Related Workmentioning

confidence: 99%

“…Study [42] tested face detection from Feret database. Study [22] tested FEI database contains facial images including facial expressions, occlusion, lighting conditions, and background complexities. Studies [23,40,43] tested on IMM frontal face database contains variance of lighting conditions which was recorded in 2005 by Fagertun and Stegman at Technical University of Denmark.…”

Section: Related Workmentioning

confidence: 99%

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Soon¹,

Basari²,

Ibrahim³

et al. 2018

ijacsa

View full text Add to dashboard Cite

Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

show abstract

“…no two persons can have same faces. The facial features commonly used in many of the studies are skin color [14], spatio-temporal [15], geometry [16] and texture pattern [17]. Face detection algorithms are computationally intensive, which makes it is difficult to perform face detection task in real-time.…”

Section: Related Workmentioning

confidence: 99%