Attendance System Based on Face Recognition Dependent on Deep Intelligent Techniques

The proposed technology enabled the PCs (Personal Computers), besides the other types to understand the visual surrounding world called computer vision. This led to the need for image processing techniques, which are related to performing many processes on images which may include enhancing, denoising, segmentation and other methods. One of the main applications of image processing is face detection, which is used in many applications nowadays. The face recognition technique usually depends on extracting facial features from the detected face.

According to the more sensitive nature of face recognition technology, it is very important to keep in mind that this confidential data must be handled with great care and caution regarding the academic areas that are concerned with students’ belongings and their data regarding methods of impersonating some personalities or attending exams on behalf of other students by exploiting similarities Therefore, many details in the face should be considered in any face recognition to increase safety, reliability and accuracy in identifying characters. The persons, whose photos were posted later on, agreed to use their photos in this study.

Most of the environments and fields have become in touch with the detection and vision techniques, especially when the cameras have to be used. The given data from cameras are used today to detect objects, persons, cars, surveillance systems, traffic monitoring systems, security systems, moving object detections [1, 2], robotic systems [3, 4], and sustainable applications [5]. The real-time systems, which depend on the usage of cameras, are mostly behaved with real video that consists of multi-images displayed in a high frequency. These images have to be captured during their display and listed according to their content. The object or a person is detected inside the captured image according to many processing and operations due to the desired application. The most important part of the person’s detection process is the face; as the face is mostly captured from an image or a video source. Besides, face detection is usually considered as a part of the computer vision field which is known as face detection or recognition. Face detection or face recognition has become popular in recent years with the development of computerized power. This type of detection has a very expanded application range as it includes retrieval systems of a content-based image, biometrics, video processing and photography [6]. People recognition is one of the most important topics in the field of biometric engineering. The face is among the different types of biometrics used for human recognition. This type of biometric, which is ubiquitous, can be acquired in environments as it is unconstrained while providing features that are strongly discriminative for the recognition. For that reason, face recognition has become a very important tool which is used for increasing the capabilities of automatic surveillance video, security systems, and software video analysis, inside thousands of applications in daily life such as entertainment, automatic face tagging in the collections of photos, and smart shopping [7]. In this paper, a proposed method, which gave robust performance and accurate results, especially in real-time applications, is presented. This study adopts a Convolution Neural Network (CNN) as its algorithm can extract the features and classify the objects (faces) in predefined classes. The Haar-like is used in Viola Jones to extract features in face detection. Because of adopting CNN, the convolutions and max pooling techniques are used to get the best face features. Viola-Jones is selected due to its rapid face identification process and is more effective in recognizing potential face areas using Haar-like features. Regions of interest are rapidly narrowed down by this method. After that, CNNs are used for face recognition, taking advantage of their capacity to acquire complex facial traits and attain a high degree of accuracy. The combined method is efficient for real-time applications in a various environmental setting, it strikes a compromise between speed and precision.

The proposed methods are applied to detect and recognize the employees at the University of Mosul in Iraq.

Face detection, face recognition, and other biometric techniques were improved and developed due to their importance and rapid use in most fields. Many methods were implemented and developed for this purpose. The following are the most used dependent methods in this paper.

One of the most powerful computer vision technologies is face detection and recognition which has become revolutionary and interacted with the surrounding life due to its important applications which are usually dependent on it.

Face detection is used for human face detection in images or videos. Face detection models depend on different techniques that rely on human face features. Face detection has a wide range of applications today. It is not related to detecting human faces only, but it is related to identifying those faces to whom they belong. This is achieved by extracting unique facial features for each face and comparing them with the database of known faces. Face detection simply detects faces while face recognition identifies the face.

3.1 Viola-Jones face detection

Face detection problems were analyzed precisely by using Viola and Jones methods [16]. The Viola and Jones techniques include using the captured images of faces for training. These images are considered to have a size of 24×24 pixels for each. The mentioned method assumes that the relevant features could appear or arise anywhere in each one of the dependent images. Firstly, they excluded and were far from concentrating on the clear “obvious” features. Ears, eyes, mouth, and nose have to be detected as features. Additionally, they usually exclude the colors and skin-tone during the detection, preferring to analyze the faces in terms of intensity characteristics' profile. The Viola-Jones method is based on many steps of processing, which includes the process of obtaining the integral image using cascaded classifiers which are mostly “weak”. This method or technique is called "Haar". Generally, there are three techniques used by the Viola-Jones method [16], these techniques are:

• Haar technique, which is usually used for the features’ extraction step that is considered the cause of obtaining an integral image.
• Machine learning techniques, which are called "Ada Boost". It can be used for face detection. The basic classifiers are built due to the complexity of the classifiers and the use of boosting techniques.
• The Cascade classifier, which usually combines many features efficiently.

In this method, the obtained classifier has many filters. When the best classifiers are cascaded, the regions "pixels" of the face and the regions of non-face should be separated. The object detection cascaded framework controls face detection. The most important advantage of the proposed idea is that it can detect faces regardless of the illumination condition. The feature extraction operation (Haar filter is used here), which uses the Haar cascaded classifier, is the main part of this step. The Haar features are mostly giving the impression that at least one feature is presented in the dependent image. Each of these features returns a unique value, which is obtained by calculating the difference between the summation of pixels in the white space region, and the summation of pixels in the black space region. In order to decrease the processing time of the features’ face detection, Haar’s features are considered as a rectangular-shaped region. Many of Haar filters (Figure 1) are usually used in face detection, which include the following steps.

The first step is represented in Haar-like features selection; the selected image is fed to the system as input data (Figure 2). The procedure of this step involved a scan process for all the parts of an image, beginning from the top left pixel (point) to the last point in the right bottom. In this process, and until scanning of the last point, an examining process is applied to ensure that all the face’s features are presented. To detect and select many human faces in images, the scan process has to be repeated many times using Haar features. In this method, the rectangular features are selected and calculated rapidly, due to the integrated image usage. Whenever the mentioned method is applied, four values are expected to be used, these values are used to represent the corners (points of the mentioned rectangle) [12]. Referring to Figure 1, the values at points (a, and b) are given by calculating the summation of all the pixels that are located above and left the points (a, and b).

In Figure 2, Haar-like features appear clearly. There is only one region that includes the letter (A) written in black space. Thus, as an equation, it can be written as: f₀ (x)=A.  In Figure 3, two other examples of filters can be shown.

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Figure 1. Haar features

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Figure 2. Haar-like features

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Figure 3. Haar like filter

The filters shown in Figure 3, are given according to the equations f₁(x), and f₂(x), where f₁(x) = (B – A - C), and f₂(x) = (B + C - A*D)". In the Voila Jones algorithm, a window with a 24×24 pixels size has been used as a based window for the purpose of feature evaluation in the given image. Generally, and due to the use of this mentioned window that has dimensions of "1, 60, 000", features cannot be calculated. Thus, the method of the Ada boost is dependent and used as a technique of machine learning to complete the features calculation. Referring to the mentioned case, the Ada Boost machine learning efficiently selected the best features, out of "1, 60,000" features. The Ada boost machine learning is formed as the strongest classifier due to the combination technique which is applied for the linear parts of the weak classifier, which is given as:

$F(x)=\alpha 1 F_1(x)+\alpha 2 F(x)+(2)$

In case the image passes through all of the stages successfully, it can be identified as a person's face. However, it cannot be classified as a person's face when it fails at any one of these mentioned stages. This situation obtains the final description of the algorithm those are used [10, 17]. Viola-jones is a very effective method for face detection, while Haar features are more effective when used by Viola Jones in extracting face features, especially for real-time face detection.

Simple patterns called Haar features are used to represent the changes in intensity between neighbouring areas in a picture; they seem like rectangular pieces of black and white covering particular parts of a face. These designs are intended to draw attention to the common edges, lines, and textural variations seen in facial structures. Two, three, and four rectangle features are a few instances of Haar features for face detection:

• Two-rectangle features: Draw attention to contrasts such as the darker pupil inside the lighter iris or the brow's border over the eye region.
• Three-rectangle features: Can be used in identifying the nose bridge between the eyes or the darker nostrils beneath the lighter nose bridge.
• Four-rectangle features: May draw attention to the corners of the lips, or the difference between the cheeks and the mouth area.

The total pixel intensities in the white regions are abstracted from the total pixel intensities in the black regions to compute a Haar feature. This difference yields a numerical number representing the contrast level of the particular facial feature. In face detection, Haar features work well for the following reasons:

• Facial Contrasts: Haar features those able to capture the constant patterns of bright and dark regions, are found in human faces.
• Computational Efficiency: Integral pictures are allowed for speedy computation, which makes them appropriate for real-time applications.
• Feature Diversity: Different facial structures and details can be captured to the diversity of Haar features.

The classifiers’ cascade is usually used by the Viola-Jones algorithm to boost efficiency even more; the more complicated features have to be concentrated on possible faces, and to reduce needless computations, simpler features are utilized to eliminate non-face regions rapidly.

3.2 CNN deep learning

The deep learning approach is used in both academic and practical application fields, it is usually used in recognition applications such as image recognition and voice recognition [18]. The Convolutional Neural Network (CNN) which is considered a deep learning method, is also used for the same purpose also, it is used for purpose of recognition like finger vine recognition [19], ECG system [20] speech emotion recognition [21], palm-print [22], Therapeutic process [23, 24], and IOT systems [25]. The main difference between traditional machine learning and deep learning is represented in their main work. Deep learning includes internally the feature extraction process, while traditional machine learning does not include the feature extraction process. Thus, it should be performed separately. One of the most popular examples of the learning machine in recent years is based on the approach of deep learning, which is known as a CNN. It can be identified as an innovative technique relatively, used in the area of computing for the purpose of analyzing visual data. Really, the CNN exploitation, which is used for face processing and face recognition is noteworthy. The CNN is used to supervise the techniques of machine learning that can extract “deep” information from the dataset by the rigorous example that is based on the training in a sense. This approach mimics how the human brain undertakes the methods’ types of learning process. The CNN has been successfully applied to the extracted features in images during the recognition of the persons’ faces, image sections' classification, and also segmentation. As shown here, the increase in using CNN in recent years goes alongside the ability of them to learn the complex features using the multi layered nonlinear type of architecture. Originally, the CNN returns to the beginning of 1990 [26]. The predominant skepticism of the gradient descent must always be over the fit. The main argument that is used for, has been a gradient based on the optimization methods, which are led to get the stuck in the local minima. In recent years, these assumptions have usually been overturned, because of the trusted results of CNN that has produced across many research domains. Due to this time, the state-of-the-art learned deep models, which are based on architectures of the CNN, are mostly being used in the visual domains that are computed [27].

A popular variant of Convolutional Neural Networks (CNN) is comparable to the multi-layer perceptron (MLP), and comprises multiple convolution layers followed by sub-sampling (pooling) layers and fully connected (FC) layers at the end. In Figure 4, an example of CNN architecture for image classification is shown. Feature extraction is a crucial procedure, it employs convolutional tools to isolate and identify distinctive characteristics within an image for analysis. The feature extraction network typically consists of numerous pairs of convolutional and pooling layers. The fully connected layer utilizes the output from the convolutional process to determine the image's class based on the previously extracted features. This CNN feature extraction model aims to reduce the dimensionality of the dataset by generating new features that consolidate the information from the initial set of features into a single, comprehensive feature [28].

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Figure 4. An example of CNN architecture for image classification

Many applications in different fields, which are considered relatively innovative, were depended on using the CNN technique. In the study [29], CNN is suggested to be used for a project of designing a smart city. In the study [30], CNN is used to design a real-time attendance system; in other hundreds of applications, the CNN is depended and continuing used for the purpose of recognition.

In this paper, the proposed method is advised and utilized for a variety of cases in a MATLAB environment, prior to its implementation with the academic data set; the method is recommended to be employed in attendance applications due to its accuracy and its limited consuming time, which make it suitable to be advocated for usage in real-time application.

Face recognition systems find extensive applications in the field of biometric technology. In this paper’s approach, the Viola-Jones method is employed for face detection known for its robustness and real-time performance. However, it does have limitations and struggles with face detection of faces at certain angles, particularly when the face is rotated by approximately 20 degrees. To address this limitation, we leverage Convolutional Neural Networks (CNN) for face classification. This approach yields impressive results, with the test stage achieving an accuracy rate of approximately 96%. In some cases, around (3-4%) of the input images may fail to be correctly classified. The proposed system demonstrates robustness in real-time face recognition, whether dealing with single or multiple faces. Nevertheless, variations in illumination levels negatively affect both the detection and recognition phases, occasionally leading to misclassifications. Although the suggested method demonstrated resilience, further researchers could investigate the use of deep learning for both face detection and recognition which is also used to overcome the problem of rotating images.