Local Binary Patterns And Its Variations Biology Essay


Abstract- Face recognition is one of the most important tasks in computer vision and Biometrics. Texture is an important spatial feature useful for identifying objects or regions of interest in an image. Texture based face recognition is widely used in many applications. LBP method is most successful for face recognition. It is based on characterizing the local image texture by local texture patterns. In this paper performance evaluation of Local Binary Pattern (LBP) and its modified models Multivariate Local Binary Pattern (MLBP), Center Symmetric Local Binary Pattern (CS-LBP) and Local Binary Pattern Variance (LBPV) are investigated. Facial features are extracted and compared using K nearest neighbour classification algorithm. G-statistics distance measure is used for classification. Experiments were conducted on JAFFE female, CMU-PIE and FRGC version2 databases. The results shows that CS-LBP consistently performs much better than the remaining other models.

Keywords- Face recognition, local binary pattern (LBP) , Multivariate Local Binary Pattern (MLBP), Center Symmetric Local Binary Pattern (CS-LBP), Local Binary Pattern Variance (LBPV).

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Facial recognition plays a vital rule in human computer interaction [4]. A Face recognition system can be either verification or an identification system depending on the context of an application. The verification system authenticates a person's identity by comparing the captured image with his/her own templates stored in the system. It performs a one to one comparison to determine whether the person presenting himself/herself to the system is the person he/she claims to be. An identification system recognizes a person by checking the entire template database for a match. It involves a one to many searches. The system will either make a match or subsequently identify the person or it will fail to make a match.

The human ability to recognize face is remarkable. We can recognize the thousands of faces learned throughout our lifetime and identify familiar faces at a glance even after years of separation. This skill quite robust, despite large changes in the visual stimulus due to viewing conditions ,expressions ,aging and distractions such as glasses or changes in hairstyle or facial hair. Existing biometric systems are developed for corporate user applications like access control, Computer logon, Surveillance camera, Criminal identification & ATM.

Face recognition system can be grouped as 1.structure based 2.appearance based .In structure based method [12] a set of geometric face features, such as eyes, nose, mouth corners, is extracted, the position of the different facial features form a feature vector as the input to a structural classifier to identify the subject. In the second method [2], the appearance of face as input to decision making and they can e further categorized as holistic and component based. The holistic appearance methods operate on the global properties of face image. Nowadays ,appearance based methods not only operate on the raw image space ,but also other spaces ,such as wavelet ,local binary pattern and ordinal pattern spaces.

The Local Binary Pattern is originally proposed by Ojala [7] for the aim of texture classification, and then extended for various fields, including face recognition [9], face detection [3], facial expression recognition [13].The Local Binary Pattern is a non parametric operator which is used for describing a local spatial structure of an image. The Local Binary Patter method is computationally simple & rotation invariant method for face recognition [9].Adaptive smoothing for face image normalization under variation of illumination is presented by Y.K.Park [8]. The illumination is estimated by iteratively convolving the input image with a 3-by-3 averaging kernel weighted by a simple measure of the illumination discontinuity at each pixel. In particular, weights of a kernel are encoded into a local binary pattern (LBP) to achieve fast and memory efficient processing.

Face image is divided into several regions and LBP is applied and features are extracted over the region. These features are concatenated to form face descriptor [10]. Although face recognition with local binary pattern has been proven to be a robust algorithm, it suffers from heavy computational load due to the very high dimensional feature vectors that are extracted by concatenating the LBP histograms from each local region. A new multichannel filter based Gabor wavelet is designed based on theory and practicality. Its center frequency is the range from low frequency to high frequency, its orientation is 6 and scale is 6. It can extract the feature of low quality facial expression image target, and have well robust for automatic facial expression recognition [5].

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MLBP is proposed by Arco Lucifer [1] for texture segmentation. Most of the images are multiband in nature. So this method is widely used for image classification and segmentation. CS- LBP method was introduced by Marko Heikkila [6]. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas and computational efficiency. LBP variance (LBPV) is proposed by Zhenhua Guo [14] to characterize the local contrast information into one dimensional LBP histogram. In this paper LBP & its variants methods are evaluated in JAFFE female database for face recognition. Among these methods, the best method will be tested by CMU PIE, FRGC version2 databases.

The rest of the paper is organized as follows. Section II reviews about LBP, MLBP,CS-LBP and LBPV. Section III explains about classification principle. Section IV reports the experimental data & section V gives the experimental results on JAFFE female, CMU-PIE & FRGC version2 databases. Section VI gives the conclusion of this paper. Section VII gives the references used in this paper.

Local binary pattern & its variations

Local binary pattern(LBP)

Local Binary Pattern was introduced by Timo ojala [11]. The standard version of the LBP of a pixel is formed by thresholding the 3X3 neighborhood of each pixel value with the center pixel's value. Let gc be the center pixel gray level and gi (i=01,..7) be the gray level of each surrounding pixel. If gi is smaller than gc ,the binary result of the pixel is set to 0 otherwise set to 1. All the results are combined to get 8 bit value. The decimal value of the binary is the LBP feature.

Bilinear interpolation method is used for a sampling point does not fall in the center of the pixel. Let LBPp,r denote the LBP feature of a pixel 's circularly neighborhoods, where r is the radius and p is the number of neighborhood points on the circle.

The concept of uniform patterns is introduced to reduce the number of possible bins. Any LBP pattern is called as uniform if the binary pattern consists of atmost two bitwise transitions from 0 to 1 or vice versa. For example if the bit pattern 11111111(no transition) or 00110000 (two transitions) are uniform where as 10101011 (six transition) are not uniform. The uniform pattern constraint reduces the number of LBP pattern from 256 to 58 and it is very useful for face detection [10].

Multivariate Local binary pattern(MLBP)

The Multivariate Local Binary Pattern operator, MLBP c was developed by Arco Lucifer [1] which describes local pixel relations in three bands. In addition to the spatial interactions of pixels within one band, interactions between bands are considered. Thus, the neighborhood set for a pixel consist the local neighbours in all three bands (Fig 3).

The local threshold is taken from these bands, which makes up a total of nine different combinations. This results in the following operator for a local color texture description. The color texture measure is the histogram of MLBP c occurrence, computed over an image or a region of an image. This single distribution contains P Ã-32bins (e.g. P =8 results in 72 bins).

Center Symmetric Local binary pattern(CS-LBP)

The CS-LBP is another modified version of LBP. It model was developed by Marko Heikkila [6] for the recognition of object in PASCAL database. The original LBP was very long its feature is not robust on flat images. In this method, instead of comparing the gray level value of each pixel with the center pixel, the center symmetric pairs of pixels are compared. CS-LBP is closely related to gradient operator. It considers the grey level differences between pairs of opposite pixels in a neighborhood. So CS-LBP take advantage of both LBP & gradient based features. It also captures the edges and the salient textures.

The CS-LBP features can be computed by

Where gi and gi+n/2 correspond to the gray level of center symmetric pairs of pixels (N in total) equally spaced on a circle of radius r. It also reduces the computational complexity when compared with basic LBP [6].

Local binary pattern variance(LBPV)

The LBPV descriptor proposed by Zhenhua [14] offers a better result than LBP. Local invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. LBPV proposes an alternative hybrid scheme; globally rotation invariant matching with locally variant LBP texture features. It is a simplified but efficient joint LBP and contrast distribution method. LBPp,r/VARp,r is powerful because it exploits the complementary information of spatial pattern and local contrast. Threshold values are used to quantize the VAR of the test images computed to partition the total distribution into N bins with an equal number of entries. These threshold values are used to quantize

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the variance of test images.


A. Training

In the training phase, the texture features are extracted from the samples selected randomly belonging to each face class, using the proposed feature extraction algorithm. The average of these features for each face class is stored in the feature library, which is further used for classification.

B. Texture similarity

To find out the similarity between training models & testing sample G-statistic distance measure is used. Similarity between the textures is evaluated by comparing their pattern spectrum. The spectrums histograms are compared as a test of goodness-of-fit using a non-parametric statistics, also known as the G-statistics [7].The G statistic compares the two bins of two histogram and is defined as

C. Classification

In the texture classification phase, the texture features are extracted from the test sample x using the proposed feature extraction algorithm, and then compared with model feature using K-Nearest Neighbor classification algorithm .In experiment 1, K=1 is used. (ie) minimum distance classifier is used. Minimum distance between the model feature value & the sample feature value is calculated.

Experimental data

In order to access the discrimination capability of any method is done by experimental tests using the same data presented in Figure 5.

Fig 5 Sample Images from JAFFE Female database

Fig 6. Samples from the CMU-PIE face database. The first image from the left is a sample training image and the others are the sample testing images.

Fig. 5 shows the images from JAFFE female database. Fig.6 represents the images from CMU-PIE database. Among these five images only one is used for training and the remaining four images are used for testing purpose.

Fig 7. Samples from the FRGC Version2 face database. The first image from the left is a sample training image and the others are the sample testing images.

Similarly Fig 7 shows the images from FRGC version2 database. Here first image from the left side is used for training and the remaining images are used for testing phase.


Experimental comparisons on JAFFE Female database:

TABLE 1 recognition rate for different window size

No of testing Samples=30

Experiments are conducted on JAFFFE database by varying the window size and also varying the number of input samples from each image. During experiment#1 the number of training sample is fixed as 10 and the window size is varied from 10X10. During experiment#2, the window size is fixed as 30X30 and the number of testing sample is increased from 10. Table 1 & Table 2 shows that recognition rate increases with the increase in window size as well as the increase in the number of samples taken for classification. Our experimental results show that CS-LBP provides better results than the remaining other methods.

Experimental comparisons on CMU-PIE & FRGC Version2 databases for illumination variation

Experiment#3 is conducted on CMU-PIE and FRGC version2 database which is shown in Fig.6. The first image from the left side is taken as training image and the remaining four images are used as testing images. In training phase, facial features are extracted by CS-LBP method and stored in the database. During testing phase, facial features are extracted by using the above method and the difference between two facial features is evaluated by G-statistic distance measure with k=1(nearest neighbour classification) algorithm. This experimental results show that face recognition is mainly depends on illumination changes.

TABLE 3 and 4 shows the recognition rate vs window size of CS-LBP method on CMU-PIE & FRGC Version2 databases. It shows that recognition rate increases with increase in window size. CMU-PIE database gives better results than FRGC Version2 database under different lighting conditions.

TABLE 3 classification accuracy of CMU-PIE DATABASE BY CS-LBP METHOD

TABLE 4 classification accuracy of CMU-PIE & frgc vERSION2 DATABASE BY CS-LBP METHOD


LBP is grey scale invariant and rotational invariant. This property is well suitable for many applications. The facial recognition based on Local Binary Patterns is extremely simple. In this paper LBP and its modified models CS-LBP, MLBP and LBPV were analysed. CS-LBP performs very well and gives the recognition rate of 70% with the JAFFE female database. CMU-PIE and FRGC Version2 databases are experimented by the same model under different illuminations. The model gives the recognition rate of 50% for CMU-PIE and 46% for FRGC Version2 database. CS-LBP provide good recognition rate than other methods and also it consumes less computational time.

vii .References

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