基于多核学习的画像画风的识别

Drawing Style Recognition of Facial Sketch Based on Multiple Kernel Learning

画像的画风识别广泛应用于名画甄别和刑侦破案领域.文中提出基于多核学习的画像画风的识别算法.首先根据艺术评论家从画像部件的处理方式鉴定画像画风的方法,从画像中提取脸、左眼、右眼、鼻和嘴5个部件.然后根据画家从画像的明暗度和画像作者的绘画笔法识别画像画风的方法,从每个部件上提取灰度直方图特征、灰度矩特征、快速鲁棒特征和多尺度的局部二值模式特征.最后通过多核学习将不同部件和不同特征融合以进行画像画风的识别.实验表明,文中算法性能较好,能取得较高识别率.

The drawing style recognition of facial sketches is widely used for painting authentication and criminal investigation. A drawing style recognition algorithm of facial sketch based on multiple kernel learning is presented. Firstly, according to the way of art critics recognize the drawing style of facial sketch, five parts, the face part, left eye part, right eye part, nose part and mouth part, are extracted from the facial sketch. Then, gray histogram feature, gray moment feature, speeded-up robust feature and multiscale local binary pattern feature are extracted from each part on the basis of artistsˊ different understandings of lights and shadows on a face and various usages of the pencil . Finally, different parts and features are integrated and the drawing styles of facial sketches are classified by multiple kernel learning. Experimental results demonstrate that the proposed algorithm has better performance and obtains higher recognition rates.