Texture features for clustering based multi-label classification of face images

Mohammed, Ahmed Abdulateef; Xiamixiding, Reziwanguli; Sajjanhar, Atul; Chen, Juan; Nasierding, Gulisong

doi:10.1109/cisp-bmei.2017.8301972

Cited by 3 publications

(1 citation statement)

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“…Definition 1, a repeating pattern formed by arranging elements or primitives according to specific rules. Definition 2, if a set of local attributes of an image function are constant, or slowly varying, or approximately periodic, then the corresponding region in the image has a constant texture [ 10 ]. The study of this kind of surface texture is called texture analysis.…”

Section: Methodsmentioning

confidence: 99%

Age prediction based on a small number of facial landmarks and texture features

Wang¹,

Chen²

2021

THC

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BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.

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Section: Methodsmentioning

confidence: 99%