Self-paced learning face age estimation method based on noise elimination

Abstract

The invention discloses a self-paced learning face age estimation method based on noise elimination, and belongs to the field of computer vision and machine learning. The human face images often havechanges such as postures, illumination, expressions, shielding, dislocation and the like; the face picture is divided into a simple picture (the absolute error between the predicted age and the actualage is small) and a difficult picture (the absolute error between the predicted age and the actual age is large); under a self-pacd learning framework, a strategy from a simple picture to a difficultpicture is adopted to train a deep regression network to establish a nonlinear mapping relationship between facial features of a human face and a target age; meanwhile, a cap () function provided bythe invention can eliminate noise images in a training sample; according to the method, a cap () function, self-paced learning and a deep regression forest are fully utilized, it is guaranteed that the extracted facial features have high representation capacity, and the accuracy and robustness of an existing method are improved. The method can be applied to the aspects of human-computer interaction, age-based security control, social network entertainment, age differentiation advertisement and the like.

Description

technical field [0001] The invention belongs to the technical field of computer vision, relates to face age estimation technology, and is mainly applied to aspects such as human-computer interaction, age-based security control, social network entertainment, and age-differentiated advertisement. Background technique [0002] Face age estimation technology refers to the technology of automatically estimating the age of faces after analyzing the features of faces through computer algorithms. Since this technology can be widely used in human-computer interaction, age-based security control, social network entertainment, and age-differentiated advertising, it is a hot research topic in the fields of computer vision and machine learning in recent years. The existing face age estimation algorithms are mainly divided into methods based on shallow models and methods based on deep learning. [0003] The rationale behind the shallow model-based approach is to decompose the task into t...

AI Technical Summary

Problems solved by technology

Obviously, during the training process of the age estimation task, the impact of noisy face images (with changes in pose, lighting, expression, occlusion, and misalignment) on the entire model is huge, but so far it has not yet emerged how to mitigate this. method of influence, the present invention will work around this angle

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