Research on face recognition models has a long history

and has been successfully used in cosmetic surgery and other fields. Meanwhile

exploration of facial similarity measure methods has been conducted in the fields of computer vision and pattern recognition. Some applications

such as face recognition

face retrieval

and similar face search applications

have been widely used in many fields. Existing methods that measure face similarity are typically confined to calculating image similarity without incorporating face cognition patterns used by human beings. Therefore

results of current methods are usually less than optimal from the perspective of human cognitive habits. Based on face cognition patterns

each kind of facial feature

such as eyes

nose

mouth

and facial shape

is divided into several common types. For example

eyes are divided into 10 groups such as phoenix

circle

triangle

and others. After analyzing feature vectors of different types

we pick 20 images from CAS-PEAL-R1 database for each common type of every facial feature to locate feature points and calculate feature values. We use statistical values of the results to construct facial feature classification models

i.e.

contour similarity measurement models for facial features. Contour information cannot include certain facial details

e.g.

fold eyelid

high nose bridge

and so on. To measure similarity of details between two faces

we employ a circular local binary pattern operator to calculate the texture similarity of corresponding facial features. A combination of contour and texture similarities is used as criterion for a similar face search. Our test face database contains 80 frontal neutral and head-angle face images collected from the Internet; these images are different from the aforementioned training images. Our target face database consists of two parts: 1 040 frontal images in the CAS-PEAL-R1 database and 102 star identification photos collected from the Internet. Only a few CAS-PEAL database images are allowed to be presented in papers

so extra star photos are added to the target face database. We use our method and the method provided by Face++

which presents the highest level of similar face research

to search most similar faces for each test-face image from the target face database. Statistically

the overall accuracy of our method is higher than that of Face++. TOP1 and TOP2 retrieval results are obviously better than Face++

with accuracy rate gaps both reaching more than 12%. Experimental results show that the search results of our method are more satisfactory from the perspective of human cognitive habits. Thus

our method can be applied to search similar faces for frontal neutral and head-angle face images. Besides

the proposed image search approach based on cognitive models can also be applied in the business sector

e.g.

image-based similarity search of online goods.