Explore Semantic Pixel Sets Based Local Pattern with Entropy Information for Face Recognition and Application in Back Propagation

In this paper, a combination of the Local Binary Pattern method with a semantic pixel set of entropy information is used on back propagation networks for face recognition. This method divides the sample data into NxM zones and calculates the feature value of each zone. In this paper the sample data is divided into 6x9 zones, ie 54 zones with the size of each zone is 10x10 pixels. The LBP method is a uniform pattern of each zone and makes comparisons to zones that have the most number of active pixels. Then from the LBP method the semantic method is used to extract the entropy information from each image. From the feature extraction, there are 107 feature values, 54 of LBP and 53 methods of semantic method. The value of the feature is used as input for classification using back propagation networks. 100 sample data were used for training and 60 different sample data were used for recognition level test. From the test conducted got the recognition rate using a combination of two methods of this extraction feature is 98%.


INTRODUCTION
Current digital image processing is not only limited to digital image processing, but also includes character recognition techniques such as alphanumeric characters, handwriting, etc. (Emanuel and Hartono, 2008). In addition to writing recognition, facial recognition is also one of the interesting topics to be used as an area of research in the field of image processing and pattern recognition in recent years. Face recognition is a popular biometric technique. Because it can be applied in several applications, for example: application access control, video, and human interaction with computers (Jain and Li, 2005).
The initial stage for facial recognition is image processing and feature extraction. The selection of a good feature extraction method is one of the important factors to achieve a high level of recognition (Jain and Taxt, 1996). Feature extraction is an important factor in the face recognition process but to get strong features to describe facial images is still a problem (Zhao et al, 2003). Local descriptors that are known at this time, such as the Gabor wavelet method are one of the best performances, but the method still requires too much processing time in the extraction feature. (Serrano et al, 2010) while Local Binary Pattern (LBP) is more popular, very simple, and very fast to calculate (Pietikäinen et al 2011).
Vol.6; Issue: 6; June 2019 Most methods used such as LTP, TP-LBP, and Multi-scale LBP, where to represent faces have not got maximum results due to variations in expression. Semantic pixel method set Local Binary Pattern (sps-LBP) is an approach to solve image block problems involving variations in expression, sps-LBP is the development of modified LBP. Sps-LBP is one approach that is intended to solve the problem of dividing blocks using the simple clustering method to divide pixels into an area by assuming the value of intensity. In sps-LBP the face image is divided into a number of square regions of origin and then the pixels in each area of origin are again grouped in a semantic manner. The workings of the semantic pixel set method based on LBP is to divide the pixels in an area of origin of images into sets (sets) so as to make it better to use spatial information when building histograms (Chai et al, 2012). In this study for each sample data that has gone through the image processing process, feature extraction will be carried out. First, the Local Binary Pattern (LBP) operator is used for image texture classification and then applied for facial recognition, the semantic pixel method is a method for grouping pixels into sizes selected based on the image used LBP method, after that the image is extracted using probability density function (PDF) to calculate entropy information. The ability of the semantic value by taking a description of the image from entropy information using the PDF function was tested to recognize image images with the next step using backpropagation artificial neural networks. It is expected that with these two feature extractions it can increase the level of recognition in terms of facial recognition.

LITERATURE REVIEW Image
Image (image) is an image in a twodimensional field. Viewed from a mathematical point of view, the image is a continuous function of the light intensity in the two-dimensional field (Munir, 2004). The light source illuminates the object, the object returns to reflect part of the beam of light. This reflection of light is captured by optical devices, such as the eyes in humans, cameras, scanners (scanners) and so on, so that the image of an object called the image is recorded. Digital imagery is a visual representation of an object after experiencing various transformations of data from various forms of numerical circuits. Digital images can be classified into several types: 1. Binary imagery Binary imagery (binary image) is an image that only has two gray values: black and white. The object pixels are 1 and the background pixels are 0. When displaying images, 0 is white and 1 is black. To change the grayscale image to a binary image, the process carried out is to change the quantization of images by means of globalimage thresholding. Each pixel in the image is mapped into two values, 1 or 0. With the floating function: 2) with I (x, y) is the gray level of a coordinate obtained by setting the color R (red), G (green), B (blue) indicated by the parameter values α, β and γ. In general the values of α, β and γ are 0.33. Other values can also be given for the three parameters provided that the total is 1 (Putra, 2009 Image processing Image processing is processing or an attempt to transform an image / image into another image using certain techniques.

Feature Extraction
Feature extraction is the process of measuring data that has been normalized to form a feature value. Feature values are used by classifiers to recognize input units with target output units and facilitate classification because these values are easy to distinguish (Pradeep et. Al, 2011). Another definition of feature extraction is one of the most important steps in the face recognition process, but to get discriminatory and strong features to describe facial images is still a problem for now. Broadly speaking, features are all measurement results that can be obtained. The feature can also describe the characteristics of the object being monitored (Putra, 2009). An example of a low level feature is signal intensity. Features can be either symbol, numeric or both. An example of a symbol feature is color. An example of a numerical feature is weight. Features can be obtained by applying the feature search algorithm to the input data. Features can be expressed with continuous, discrete or discrete-binary variables. Binary features can be used to indicate the presence or absence of a particular feature.

Artificial Neural Network Artificial
Neural Networks are computational networks that simulate nerve cell networks (neurons) from the central nervous system of living things (humans or animals) (Graupe, 2007). Artificial neural networks were first designed by Warren Mc-Culloh and Walter Pitts in 1943. Mc Culking-Pitts found that combining many simple neurons so that they become a nervous system is an increase in computational energy. Artificial neural networks are arranged with the same assumptions as biological neural networks (Puspitaningrum, 2006): Information processing occurs in processing elements (neurons). The signal between two neurons is transmitted through connection links. Each connection link has associated weights. Each neuron applies an activation function to the network input (number of weighted input signals). The aim is to determine the output signal.

Reverse Propagation Algorithm
The first Back Propagation algorithm was developed in 1986 by Rumelhart, Hinton and Williams to determine weights and is used for multi-layer perceptron training (Graupe, 2007). The back propagation method is a very good method of dealing with the problem of complicated complex patterns. . This method is a popular neural network method. Some examples of applications that involve this method are compressing data, detecting computer viruses, identifying objects, synthesizing sounds from text, and others (Puspitaningrum, 2006). The term "back propagation" is taken from the workings of this network, namely that the gradient error of hidden units is derived from re-broadcasting the errors associated with the output units. This is because the target values for hidden units are not given (Puspitaningrum, 2006). Back propagation is a supervised learning method. This method requires a predetermined value to get the desired output in the learning process. from the semantic pixel set based method. 5. Analysis using the back propagation algorithm for training images and test images so that they get the right recognition process.

Designing
The semantic pixel set-based local patterns formula takes the entropy value from the divided image in square form, the value taken is the trained image that will be forwarded to the back propagation neural network algorithm, from the back propagation it will be concluded how accurate both algorithms it goes on.

RESULTS AND DISCUSSION Implementation
Analysis and facial recognition models must be implemented into the program code to be able to find out the ability of the combination of semantic pixel methods and their entropy information.
Encoding is divided into five main modules, namely: 1. Image processing module Is a module to carry out all image processing processes and carry out feature extraction processes.

Network training module
Is a module to carry out the back propagation network training process.

Network testing module
Is a module to carry out the back propagation network testing process that has been trained. In this study, coding was done using the Python 2.7 programming language, because the Pyhon language does not have a module to do image processing so that additional complete modules are needed, check at https://www.python.org/download/releases/ 2.7/

Testing
After the implementation phase is carried out testing the ability of the combination of zoning methods and diagonal based feature extraction on the back propagation network for the introduction of handwritten numbers. To find out the addition of the level of recognition obtained by using a combination of methods, two other networks were built with the same architecture and parameters but each one uses only an extraction method, namely zoning or diagonal based feature extraction. The network that uses a combination of zoning and diagonal based feature extraction methods uses 123 feature values, a network that uses the zoning method uses 54 feature and network values using the diagonal based feature extraction method using 69 feature values as input.

Network Preparation
The three networks have the same hidden layer and output layer, 96 hidden layer neurons and 10 output layer neurons. The input layer is built according to the extraction method used, namely Semantic pixel information entropy (54 input neurons), Semantic Pixel Rate Information Entropy (69 input neurons) and its combination (107 input neurons) to be included in backpropagation. The architecture of the three networks can be seen in table 1.
Vol.6; Issue: 6; June 2019 So, according to the table for networks I and II, I will explain for the input layer stage, the value is clearly 54 and both networks are given blanks for hidden layers and output layers because it is not needed, which is only needed for network III because it is needed for training tests and tests test, then for backpropagation input a combined layer from networks I and II, for hidden layers we need 96 layers and the output layer (output layer) we only need 3 input layers. Before being tested, the three networks were trained to use training data with training parameters, namely the learning rate of 0.1, momentum 0.5 and the limit of error 0.01. The three networks are trained until the error value (MSE) achieved is smaller than 0.01. Unlike the iteration of 5000 accuracy of fixed objects and the error error is getting better with an accuracy of 0.010903. Then the results achieved for 5000 iterations are better with the least MSE, so we use 5000 iterations.

Figure 1 Accuracy of Iteration Values
In figure 1 above, it shows from the visualization of the data performed for several iterations. For example, the iteration at 3000 shows an error error value above 0.04, iterating when 4000 error errors are generated 0.02. it is better than the previous one and in the end the last iteration of 5000 produces an error of 0.01. Then it can be concluded that the larger the iteration should produce a smaller error error than the smaller iterations.

Test Result
After the three networks were trained, testing the level of recognition of the three networks was conducted. The level of introduction is tested using tested using test data, i.e. new data that is not used at the training stage. The results of the introduction of test data from the three networks tested can be seen in Figure 2. The test results from the program can be seen in table 3 below. Then we see from the table above with the image ujimark5.jpeg with the target mark, because it is known from the picture above that is Mark's image so that the target is marked as Mark, the accuracy closest to the output is Mark with an accuracy of 0.99487, then the image is tested again with a fixed target the mark with wozniak output is close to the second but the accuracy is too far and not even close to 50% at 0.00401, so the output for Chelsea island and Iqbal, with 0.00025 and 0.00001 the difference is too striking.

Figure 3 Steven Wozniak as Test Image
Source:https://www.n-tv.de/technik/Apple-1mit-allen- Komponenten-und-Originalverpackung-unterm-Hammer-article11549561.html The test results from the program can be seen in table 4.5 below. Then we see from the table above with the test image wozniak.jpeg with the wozniak target, it is known that the target image is wozniak so the target tested is wozniak, the accuracy closest to the output is wozniak with an accuracy of 0.97583, then the image is retested but the mark output close to the second but the accuracy is too far and not even close to 50%, so with the output for Chelsea island and Iqbal, the difference is too striking.

Figure 4 Chelsea Islan as a test image
Source:https://hello-pet.com/foto-chelsea-islandi-instagram-ini-bikin-hati-tenang-1823549 The test results from the program can be seen in table 5 below. Then we see from the table above with the image ujichelsea3.jpeg with the Chelsea target, it is known that the target of the image is Chelsea so that the target tested is Chelsea, the accuracy closest to the output is Chelsea with an accuracy of 0.96891, the mark output approaches the second but the accuracy is too far not even close to 50%, so with the output for Wozniak and Iqbal, the difference is too striking.