LOCAL LAPLACIAN DETAIL LEARNING FOR FACE AGING MANIPULATION

Face aging is a challenging work in image processing and computer animation field. In this paper, a novel local image Laplacian differential method is proposed to learn this complex face transformation. Firstly, together with the facial feature points correspondence, we extract the aging facial details from example face and apply to the target one by a global and a local Laplacian skin aging model respectively, and the latter makes the results more realistic in our experiment. Then, the skin color and hair style adjustments are carried out to simulate the aging process completely, making the results look more competitive and natural. Our experiment shows that our method is robust and appreciable.

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Research on a facial feature points detection method based on skin color model

2018 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2018.8408124 ◽

2018 ◽

Author(s):

Cao Danyang ◽

Zhu Menggui ◽

Zuo Yanchao

Keyword(s):

Skin Color ◽

Detection Method ◽

Facial Feature ◽

Color Model ◽

Feature Points ◽

Skin Color Model

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Three-Dimensional Measurement of Nostril Positions Based on Stereo-Camera

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.821-822.1470 ◽

2013 ◽

Vol 821-822 ◽

pp. 1470-1474

Author(s):

Bo Peng ◽

Motohiro Kano ◽

Nobuaki Nakazawa

Keyword(s):

Skin Color ◽

Detection System ◽

Three Dimensional ◽

Facial Feature ◽

Feature Point ◽

Feature Points ◽

Stereo Camera ◽

Dimensional Measurement ◽

Depth Position ◽

Three Dimensional Measurement

This paper describes the three dimensional measurement of facial feature points. The nostril part was picked up as a feature point. The humans face was observed by the stereo-camera in real time. The depth position of the nostril was derived by the binocular parallax between the detected positions of the two cameras. Here, a new detection method for a nostril was suggested. First of all, the binalization image obtained from the stereo-camera was changed to the connection ingredient by processing labeling. Next, the system narrowed down the candidate of the nostril as a facial feature point by checking the geometric characteristics such as size, the center of gravity every ingredient. Furthermore, the skin color around the nostril was utilized to enhance the detection system.

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FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v8i3.3399 ◽

2009 ◽

Vol 8 (3) ◽

pp. 887-897

Author(s):

Vishal Paika ◽

Er. Pankaj Bhambri

Keyword(s):

Neural Networks ◽

Fuzzy Inference ◽

Facial Features ◽

Facial Appearance ◽

Feature Points ◽

Inference System ◽

Detection Techniques ◽

The Face ◽

Hair Style ◽

Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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