Lip Reading: a New Authentication Method in Android Mobile Phone’s Applications

Lesani, Fatemeh Sadat; Ghazvini, Faranak Fotouhi; Dianat, Rouhollah

doi:10.18869/acadpub.jsdp.14.1.3

Cited by 2 publications

(2 citation statements)

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“…After obtaining the desired color space components, Equation () is used to identify candidate skin color areas 27 :

Skinmap = 1 / 3 ((), {Cb}^{2} + {Cr}^{2} + Cb / Cr),

where skinmap is the candidate pixels of skin color, Cb represents information about the chrominance of blue, and Cr represents information about red chrominance. After applying Equation () to an original image, an image is obtained by converting the resulting image in which the candidate points are marked for skin color.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Algorithms based on face detection using face color are highly dependent on the brightness and quality of the captured images. On the other hand, they will not present the correct response for faces whose color is not in the face's color range, for example, for black skin 27 . However, their advantages include ease of implementation and a highly accurate detection rate when the input image is suitable.…”

Section: Related Workmentioning

confidence: 99%

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A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Cui

Shi

2021

Color Research & Application

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The carpet industry is no longer a small business done in the villages on a small scale; instead, it has carved an altogether different space, identity, and appreciation for itself in the cosmopolitan world. As computers are is becoming more and more ubiquitous, most industries, including the carpet industry, use computers for quality improvement, accuracy enhancement, speed development, and cost reduction purposes. Unlike traditional carpet maps, many modern maps include images of human faces for hand‐woven carpet tableau. These digital images comprise millions of colors and thousands of pixels, making it practically impossible to construct and weave the carpet in the same dimensions. Many weavers currently use manual and experience‐based methods for reducing the size and number of hues for making a hand‐woven carpet tableau map. Therefore, the outcomes are not the optimal results and can be improved. Also, many color reduction methods do not focus on the hand‐woven carpet tableau map. To overcome these problems and gaps, this research focuses on proposing a new automatic method for reducing the size of color images without compromising facial nuances, lessening the number of colors used while protecting the important areas of the images, and transforming those images into carpet tableau maps. The proposed approach inputs the original color image. It continuously detecting the face and specifying important areas, and finally, outputs carpet tableau map that is proportional to the given dimensions and color count. To evaluate the proposed method, MATLAB, as a powerful simulation tool, was employed. Final results are compared to the existing approaches in terms of face detection, size reduction, and color quantization. The obtained results have shown that the approach improves speed by 39% in face detection and increases the precision of size reduction and color quantization phases. The results have also confirmed that when images of human faces are reduced by a proposed method to form an appropriate image for tableau maps, they are nearly always perceived as more attractive than the reduced faces via traditional methods.

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“…After obtaining the desired color space components, Equation () is used to identify candidate skin color areas 27 :