Local Octal Pattern: A Proficient Feature Extraction for Face … · 2014-04-08 · Nithya J1,...

Nithya J et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.4, April- 2014, pg. 139-146

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International Journal of Computer Science and Mobile Computing

A Monthly Journal of Computer Science and Information Technology

ISSN 2320–088X

IJCSMC, Vol. 3, Issue. 4, April 2014, pg.139 – 146

RESEARCH ARTICLE

Local Octal Pattern: A Proficient Feature

Extraction for Face Recognition

Nithya J1, Suchitra S

2, Dr. S Chitrakala

3

1,2Computer Science and Engineering, Easwari Engineering College, India

3Computer Science and Engineering, Anna University, India

1 [email protected]

2 [email protected]

3 [email protected]

Abstract— This paper presents a novel and efficient face image representation based on Local Octal Pattern (LOP) texture

features. The standard methods viz., the local binary pattern (LBP), the local ternary pattern (LTP) and the local tetra pattern

(LTrP) are able to encode with a maximum of four distinct values about the relationship between the referenced pixel and its

corresponding neighbors. The proposed method calculates the diagonal, horizontal and vertical directions of the pixels using

first-order derivatives. Thereby, it encodes eight distinct values about the relationship between the referenced pixel and its

neighbors. The performance of the proposed method is compared with the LBP, the LTP and the LTrP based on the results

obtained in terms of average precision and average recall on PubFig image database.

Keywords— face image retrieval; local binary pattern (LBP); local ternary pattern (LTP); local tetra pattern (LTrP); CBIR

I. INTRODUCTION

A. Motivation

The prodigious growth of camera devices, enable people to freely take photos to capture moments of life. By estimation more

than 60% of those photos cover human faces. The importance and the sheer amount of human face photos make manipulations

(e.g., search and mining) of large-scale human face images a really important research problem and aid many real world

applications. The designing aspects of a face retrieval system take account of noise, illumination, poses and expressions, low cost

for processing and eliminating misclassified face images. Thus, an efficient Face Image Retrieval System (FIRS) development is

required to automatically search the more relevant image from the large database.

Facial recognition system automatically identifies or verifies a person from a digital image. One of the methods is by

comparing selected facial features from a query image and a facial database. Automated FIRSs [1] typically involve finding facial

landmarks (such as the center of the eyes) for aligning, normalizing the appearance of face, selecting a suitable feature

representation, learning perfect feature combinations, and developing precise and scalable matching approaches.



Fig.1. Major steps in automatic face recognition as in [1]

The prominent step in FIRS is feature extraction whose efficiency depends upon the methods being implemented for extracting

features for a given face image. Color, texture, shape and spatial properties are the major features extracted from the face image.

There is difficulty in achieving a general representation for face images from the extracted feature, due to the fact that the

photographs are taken in different conditions such as illumination, orientation, pose, expression and aging. A comprehensive and

extensive literature survey on face detection, extraction and recognition is presented [2] – [5].

To leverage the representation of the image, the local octal pattern is calculated based on the direction of pixels using

horizontal, vertical and diagonal derivatives. By incorporating this method, we build a large-scale content based face image

retrieval system by taking advantage of the direction of the pixels. In order to evaluate the performance of the proposed system,

we conduct extensive experiments on public datasets named Pubfig [6].

B. Related Works

The local binary pattern (LBP) has been a powerful feature for texture classification and retrieval. The LBP as described in [7]

and [8] divides the face image into multiple regions for extracting the LBP feature distribution. Finally, the extracted feature is

converted into feature vector. Unlike other LBP feature extraction methods, genetic based LBP [9], attempts to reduce the size of

the feature extraction based on genetic and evolutionary computing (GEC). GEC evolves LBP extraction that has been randomly

distributed.

The forms of the LBP cannot passably deal with the presence of variations in images due to facial expression, pose,

illumination, etc. This problem has been addressed with introduction, the local ternary pattern (LTP) [10]. The proposed method

tackles the different lighting conditions by combining local feature extraction, distance transform based matching, kernel-based

feature extraction, illumination normalization and multiple feature fusion. LTP solves the sensitivity to noise to some extent by

introducing a third state. But such minimal pixel difference can be overcome by noise. To address this issue, relaxed LTP [11]

introduces uncertain state to encode the minimal pixel value.

The LBP and the LTP are able to encode images with only two and three different values. However, the LTrP [12] has been

able to encode four different values. LTrP supports varied applications like CBIR, fingerprint recognition due to its integration of

direction of pixels with respect to center pixel. The LBP, the LTP, and the LTrP translate the relationship between the center pixel and the referenced pixels based on the

distribution of edges. The maximum directions which can be coded is only four. Thus, the performance of these methods can be improved by separating the edges in more than two directions. This has led to proposal of a novel method, referred to as local octal pattern (LOP) for content based face image retrieval.



Fig.2. Calculation of the LBP and the LTP

II. FEATURE DESCRIPTORS

A. LBPs

The LBP operator thresholds the each pixel with the center pixel gc value in 3 X 3-neighborhood. It encodes 1 for gray value

greater than gc and 0 for gray value less than gc. Fig. 2 illustrates the basic LBP operator based on [13].

∑ (

)

(1)

where gc is the center pixel gray values, gp is the gray value of its neighbors, P is the number of neighbors, and R is the radius of

the neighborhood.

B. LTPs

As introduced in Tan and Triggs [10], LTP is an extension of LBP. Unlike LBP, it uses a threshold constant to threshold

pixels into three values rather threshold the pixels into 0 and 1.

The gray values when equal to the threshold are quantized to zero, those above the threshold are quantized as +1, and those below

the threshold are quantized as -1. Fig. 2 shows the calculation of LTP.

(

)

| |

C. LTrPs

Murala et al. [12] proposed LTrP that encodes the relationship between the center pixel and the referenced pixel based on the

direction of the center pixel gc. The horizontal and vertical directions are calculated using first-order derivatives.

The center pixel’s first-order derivatives (gc), along vertical (gv) and horizontal (gh) direction can be formulated as:

(4)

(5)



and the direction of the center pixel can be formulated as

(6)

From (6), the possible direction is 1, 2, 3, or 4, for each center pixel and finally the image is converted into four values.

Fig.3 and Fig.4 shows the calculation of LTrP. More details about LTrP can be found in [8].

Tetra Pattern = 3 0 3 4 0 3 2 0

Pattern1 = 00000010; Pattern2 = 10100100; Pattern3 = 00010000

Fig.3. Example of LTrP



Fig.4. Calculation of tetra pattern for the center-pixel direction “1”

III. PROPOSED METHOD

In this section, we first outline our scalable content-based face image retrieval system, and then we explain the proposed

method: Local Octal Pattern (LOP).

A. System Overview

Fig. 7 shows the overall system representation. First, system detects the location of the faces using Viola-Jones face detector [14] for all image in the database. For each detected face image, we will extract the facial components of face viz., two eyes, nose tip, and two mouth corners. A 5X7 grid is defined at each detected component, where each grid is a square patch [15]. Thereby a total of 175 grids are extracted from five components. A LOP feature descriptor is computed for each patch. Every descriptor is quantized into codewords using sparse code as described in [16], after extracting local feature. Many techniques in information retrieval can be applied in the storage of the inverted index [17]. When a query image arrives, it will go through the same procedure to obtain sparse codewords and use these codewords to retrieve images in the index system.

B. Local Octal Pattern

LOP encodes the relationship between the center pixel and the referenced pixel based on the direction of the center pixel gc.

The diagonal, the horizontal and vertical directions are calculated using first-order derivatives. An 8-bit tetra pattern for each

center pixel is obtained. From the calculated directions, patterns are divided into four parts. Finally, three binary patterns are

generated for each direction. Thus, a total of 12(4X3) binary patterns are obtained.

As described by Guo et. al.[18], using sign and magnitude components will extract more useful information. Thus, the 13th

binary pattern by using the magnitudes of diagonal, vertical and horizontal first order derivatives has been included.

Fig. 5 illustrates the flowchart of the proposed image retrieval system and Fig. 6 shows the algorithm of LOP feature

descriptor.

Fig.5. LOP System Framework



Fig.6. LOP Algorithm

IV. EXPERIMENTAL RESULTS

For experiment, images from PubFig database [6] have been used. This database consists of facial images of people taken in

different conditions. The performance of the proposed method is measured in terms of average precision and average recall.

The precision is defined as follows:

The recall is defined as follows:

where A = Number of relevant images retrieved

B = Number of relevant images not retrieved

C = Number of irrelevant images retrieved

6 𝐼 𝑜 𝑔𝑐 𝑎𝑛𝑑 𝐼45𝑜

𝑔𝑐

7 𝐼45𝑜 𝑔𝑐 𝑎𝑛𝑑 𝐼 𝑜

𝑔𝑐

8 𝐼45𝑜 𝑔𝑐 𝑎𝑛𝑑 𝐼 𝑜

𝑔𝑐

𝑀𝐼1 𝑔𝑝

𝐼 𝑜 𝑔𝑝

2 𝐼45𝑜 𝑔𝑝

2 𝐼 𝑜 𝑔𝑝

2

𝐿𝑃 𝑝− ∗ 𝑓

𝑃

𝑝=

𝑀𝐼1 𝑔𝑝 𝑀𝐼1 𝑔𝑐

Step4: Calculate the 8-bit tetra pattern for each

center pixel, and three binary pattern are generated

from them

Step5: Calculate the histograms of binary patterns

Step6: The magnitude of center pixel can be

calculated as

Step7: Construct the feature vector

Step8: A given query image and images in the

database is compared and a set of similar images is

retrieved

𝐼 𝑜 𝑔𝑐 𝐼 𝑔 𝐼 𝑔𝑐

𝐼45𝑜 𝑔𝑐 𝐼 𝑑 𝐼 𝑔𝑐

𝐼 𝑜 𝑔𝑐 𝐼 𝑔𝑣 𝐼 𝑔𝑐

𝐼 𝑜 𝑔𝑐 𝑎𝑛𝑑 𝐼 𝑜

𝑔𝑐


𝑔𝑐


𝑔𝑐


𝑔𝑐

5 𝐼 𝑜 𝑔𝑐 𝑎𝑛𝑑 𝐼45𝑜

𝑔𝑐

Step 1: Load the image and convert it into

grayscale.

Step2: Calculate the first-order derivatives in

diagonal (gd), vertical (gv) and horizontal (gh)

directions with respect to center pixel, gc.

Step3: Direction of each pixel is calculated by



Fig.8. Illustrates results of the LBP, LTP, LTrP and LOP with respect to average precision and average recall

Based on this figure, it is evident that the LOP significantly improves the retrieved results.

V. CONCULSION AND FUTURE WORK

In this paper, we have presented a novel approach named as LOP for face recognition. The LOP calculates the direction of

pixels using diagonal, vertical and horizontal derivatives.

The overall performance of LOP has been compared with the LBP, the LTP and the LTrP on facial images. The result has

improved significantly with respect to the average precision and the average recall.

Results can be further leveraged by incorporating human attributes (e.g., gender, race, and hairstyle) in a scalable framework.

REFERENCES

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[2] Yongzhong Lu, Jingli Zhou, Shengsheng Yu, “Survey of Face Detection, Extraction and Recognition” , Computing and Informatics, Vol. 22, 2003.

[3] Li Xianwei, Zhang Haiyan , “A Survey of Face Recognition Methods”, in Proc. 2nd International Conference on Computer Science and Electronics Engineering,2013.

[4] Rabia Jafri and Hamid R. Arabnia, “A Survey of Face Recognition Techniques”, Journal of Information Processing Systems, Vol.5, No.2, June 2009.

[5] V.Vijayakumari,“Face Recognition Techniques: A Survey”, World Journal of Computer Application and Technology, 41-50, 2013.

[6] N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, “Attribute and simile classifiers for face verification,” in Proc. Int. Conf. Computer Vision,

2009.

[7] T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Applications to face recognition,” IEEE Trans. Pattern Anal.

Mach. Intell., vol. 28, no. 12, pp. 2037–2041, Dec. 2006.

70

72

74

76

78

80

82

84

86

10 20 30 40 50 60 70 80

Pre

cis

ion

Number of Images

LBP

LTP

LTrP

LOP

70

75

80

85

90

10 20 30 40 50 60 70 80

Re

call

Number of images

LBP

LTP

LTrP

LOP



[8] G. Zhao and M. Pietikainen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE Trans. Pattern

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