The rapid development of face recognition has made obtaining face feature information a major research area.Local feature representation can depict the local feature information of the face in much detail.Therefore

local feature representation is widely applied in facial feature extraction.At present

the local binary pattern algorithm is the most widely used.However

LBP still exhibits some defects that are insensitive to illumination changes and noise.The local directional pattern algorithm obtains the edge gradient value by convolving the local neighborhood

which is more stable than pixel value of LBP.LDP has more advantages than those of LBP.Therefore

LDP is developing rapidly in recent years.However

the traditional LDP algorithm cannot exert a good equilibrium effect on three aspects

i.e.

sufficient feature extraction

robustness to noise and illumination

and short recognition time

at the same time.This study presents a novel approach based on double-space local directional pattern (DSLDP) algorithm for face recognition to solve the previously mentioned problems.The DSLDP algorithm can exert a good balance effect on three aspects

i.e.

feature extraction

stability

and recognition time. First

each 3×3 neighborhood pixel of the facial image gains eight edge response values by convolving the local neighborhood with eight Kirsch template operators.Eight Kirsch masks represent the eight directions of the eight sides

i.e.

east

west

south

and north corresponding to the linear edge and northeast

northwest

southwest

and southeast corresponding to the line edge.Then

the difference of each pair of neighboring edge response values is calculated to form eight new difference directions.DSLDP utilizes the direction of the largest absolute value edge gradient value of each sub-neighborhood.The two directions are encoded into a double-digit octal number to produce the DSLDP code.Finally

the face descriptor is represented using the global concatenated histogram based on the DSLDP map extracted from the face image

which is divided into several sub-blocks.Sub-blocks are weighted by information entropy.The face dimensions are reduced by principal component analysis.The nearest neighbor classifier is used to classify the faces.Then

the identification results are obtained. The face representation method is compared with recently developed algorithms

such as LDP

significance local directional patterns

enhanced local directional patterns

local directional number patterns

difference local directional patterns

center of symmetry local directional patterns

and gradient center of symmetry local directional patterns.The experimental results of the ORL

AR

and CAS-PEAL face databases show that the DSLDP algorithm exhibits better recognition performance than that of the other algorithms.The average recognition rate of the DSLDP algorithm is 97.82% on the ORL database with five test samples.The average recognition rate of the DSLDP algorithm on the AR illumination

expression

shelter A

and shelter B databases are 98.00%

98.33%

99.33%

and 98.33%

respectively.The average recognition rate of the DSLDP algorithm on the CAS-PEAL illumination

expression

shelter databases are 99.33%

95.33%

and 90.00%

respectively. 1) In the aspect of face feature extraction

the method considers not only the change in the inner edge response values but also the change in the outer edge response values.The method also fully utilizes face information extracted in the strength and the gradient spaces.2) In terms of stability

compared with other single face recognition algorithms based on LDP

the DSLDP algorithm only considers the maximum of edge response and difference edge response values.The DSLDP algorithm highlights the main edge gradient information and avoids the interference of unimportant information.Therefore

the algorithm shows strong robustness to the variations in posture

illumination

noise

and facial expression.3) In terms of recognition time

the DSLDP code is made up of a double-digit octal number;thus

the feature model number is reduced to 64 and the recognition time is obviously decreased.Therefore

the LDP algorithm can exert a good balance effect on three aspects

i.e.

face feature extraction

stability/robustness

and recognition time

at the same time.The algorithm is proved to be effective.