Secure Rotation Invariant Face Detection System for Authentication

: Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar HarmonicTransforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.


Introduction
Face recognition is an important process for facial emotion recognition, face tracking, gender classification, multimedia applications, automatic face recognition, and many others [1,2]. Many algorithms have been proposed for face detection, but many challenges with efficient and fast Nevertheless, rotated face recognition remains a challenge in practical scenarios [9][10][11]. The rotation invariant detection capability using various methodologies is summarized in Tabs. 1 and 2. So the Multi-block (MBLBP) [12] and Polar Harmonic Transforms (PHT) [1,2] techniques alone are not sufficient for fast display and rotation. For the picture illustrated in Fig. 1a. Viola-Jones algorithm [11] is not able to detect the rotated face images. LBP [13] and HOG [1,2] features are also utilized to fetch facial features of the image, but they are not rotation invariant and unable to detect the face from rotated images [14]. To address this problem, the paper proposes a Rotation Invariant Face Detection System (RIFDS) technique to detect the face from different angles of rotations [15]. RIFDS combines Polar Harmonic Transforms (PHTs) [1,2] with Multi-Block LBP (MBLBP) [12] technique for fast and accurate detection of rotated faces. MBLBP is used to extract the texture features from different angles of the image, and the PHT [1,2] method is implemented to recognize the face from any angle. MBLBP [12] extracts the features from small blocks, and these features are more précises than the features extracted from a single image as a whole [16].  Thus the features extracted from small blocks of a single image are more detailed. This leads to more accurate results. RIFDS uses binary images to display the selected facial features. When a test image is uploaded, it is converted into a grayscale image because image color increases the complexity of multiple color channels (like RGB and CMYK) [9]. RIFDS is tested on the face databases, namely JAFFF, ORL, CMU, MIT-CBCL, and LFW. The database contains images with different sizes (i.e., resolution), poses (i.e., face direction in left, right, up and down), facial expression (i.e., fear, joy, cry, anger, happiness, and sadness, shyness), and rotations (i.e., rotated at different angles). The paper is structured in four main sections: Section 1 introduces the content of the article, Section 2 presents the proposed method, Section 3 validates it experimentally, and lastly, Section 4 concludes the paper.

Binary Images
It uses two colors (black and white) and two-pixel values, i.e., 0 and 1. A binary image with m number of rows and n number of columns has N pixels and is given by Eq. (1). They display the extracted edges and other facial features in the Multi-Block LBP. When the LBP operator is applied to a digital image, detected edges are shown with white pixel values, and the rest of the image is the background. Different facial features from digital images are extracted by using the LBP operator as shown in Fig. 1b, in which extracted features (i.e., edges) are shown in white color, and the rest are the background.

Multi-Block Local Binary Pattern (MBLBP)
It detects faces from digital images through the concept of head and face boundary extraction. It can detect faces at a 15 • angle (i.e., an image with a pose left side or right side) and 360 • (i.e., frontal face) [12]. It is also used to encode the rectangular region's intensity using a local binary pattern [17]. LBP looks at nine pixels at a time (i.e., a 3 × 3 window of image = 9-pixel values) and 2 ∧ 9 = 512 possible values (see Fig. 2). MBLBP allows 256 types of different binary patterns to be formed for edge detection and face detection from images. The MBLBP operator is computed to identify the rectangle by comparing the central's rectangle average intensity, k c , with those of its neighborhood rectangles {k 0 ,. . .,k 8 }. In this way, a binary sequence is generated. The MBLBP value is obtained by Eq. (2).
where, k c is the average intensity of center rectangle and k i (i = 1..8) are the intensity of neighborhood rectangles.

Polar Harmonic Transforms (PHTs)
They are used for feature extraction and generate an invariant rotation feature. According to it if f(r, θ) represents a continuous image function on a unit disk D = {(r, θ) : 0 ≤ r ≤ 1, 0 ≤ θ ≤ 2 . The PHT with m repetition and order n is given by Eq. (4).
The radial part R n (r) of image is given by Eq. (6).
R n (r) = cos(π nr 2 ); for PCT sin(π nr 2 ); for PST With the help of PHTs, non-frontal faces are detected at different angles of rotation of faces (i.e., ±30 • , ± 45 • , ± 60 • , ± 90 • , ± 120 • , ± 135 • , ± 150 • , 180 • , ± 210 • , ± 225 • , ±240 • , ±270 • , ±300 • , ±315 • , ± 330 • ± 360) Gradient direction histogram (HOG) features can be used for face recognition under non-restrictive conditions [18]. HOG is a feature descriptor used in image and vision processing for face and object detection. The technique measure incidences of gradient alignment in localized part of the test image. This method is comparable to that of edge orientation histograms or scale invariant feature transform descriptors, and shape contexts. The major variance with other techniques is to compute on a dense grid of homogeneously spaced cells and uses touching local contrast normalization for better-quality accuracy. Tab. 1 demonstrates various face detection methods to detect the rotated faces. It has been shown that Viola-Jones, HOG features, LBP features, and Multi Block-LBP features are not rotation invariant (i.e., unable to detect rotated faces). On the other hand, Polar Harmonic Transforms (PHTs) is rotation invariant (i.e., detect the rotated faces). Tab. 2 represents features supported by different methods used for the detection of faces.

Pre-Processing Framework
The RIFDS system combines two methods PHT and MBLBP. MBLBP is used to extract texture patterns from the digital image, while PHT keeps rotation invariant characteristics [13]. This process is illustrated in Fig. 3. Here, a query image is selected to detect the rotated face from the sample data set. Then, pre-processing operations like morphological operators and classification are performed to the query image for fast processing. The query image is rotated at a 45 • angle to make it ready for analysis. The facial entities are selected as features (i.e., eyes, nose, and mouth) from the modified image. Facial features are selected and extracted for the training of the face recognition system. Face detection is applied to the selected features. The rotated face is generated and finally cropped at a 45 • angle. The sample dataset is chosen randomly.

Face Detection at Different Rotations
PHT technique is used to detect faces at different rotations. PHT is robust to noise, minimum information redundancy, fast and accurate face detection technique at different angles. So basically after selection of the test image and selecting angle with initial morphological operation images have been processed. The PHT techniques and cascading have been performed. Finally, detection of faces at various angle has been achieved. The steps for the algorithm are shown in Algorithm 1 and  4) is computed by Eq. (8).
where, N-1 and The image is reconstructed using the inverse transform function given in Eq. (10). Where min and max are the minimum and maximum values of p and q for PHT. G (x i , y k ) is the reconstructed image of the original image G(x i , y k ). The mean square for the image is computed by Eq. (11).

Facial Features Extraction and Detection
The Multi-Block LBP is used for facial feature extraction and detection. Initially test image has been selected and after rescaling processed by dividing into blocks. Comparison and binary numbers have been concluded. Further with MBLB and cascading of facial extraction the detection of faces have been performed. The local binary operator is used for the calculation of binary patterns in digital images. Extracted features of the input image are displayed using the binary image. The calculation of the local binary pattern is shown in Fig. 2. Comparison of neighboring pixels is done with the center pixel. If the neighbor pixel value is more than or equal to the center pixel value, then assign 1; otherwise, assign 0. The steps to calculate the multi-block local binary pattern for face facial feature extraction and detection are given in Algorithm 2. Figs. 7 and 8 show the detection of the face using Multi-Block LBP.   In MBLBP, feature extraction performance also depends on the number of blocks or scale size used to form the filter from the operator. Its' detection process is shown in Fig. 9. In MBLBP, s is denoted as the parameter, which is the scale of the MBLBP operator. The feature extraction is implemented with three different scales (3 × 3, 9× 9, 12× 12, and 21 × 21). By using different block sizes, it can be observed that if the scale is small, i.e., (3 × 3), it works very effectively, but it cost more than others. The average size filter (9 × 9) is computed effectively and works very fast. It also works better on noise present in the image. If large-size filters are chosen, they are easy to implement and costs less. But a large amount of discriminative information will be lost. Tab. 3 shows the performance of MBLBP with different block numbers.

RIFDS Algorithm Description
The RIFDS approach of the face detection system is shown in Algorithm 3 and Fig. 10. It can detect faces at different angles of rotation with accuracy (i.e., ± 30          Fig. 18. In Fig. 20, the results on LENA face dataset with image resolution as 512 × 512 are shown. Fig. 21 shows the result analysis of the proposed algorithm along with accuracy and time analysis. The face detection time comparison is shown in Tab. 7. Tab. 8 shows the comparison of RIFDS with PHT. In the PHT face reorganization method, feature extraction is done from a complete image. One issue in this idea is that it did not extract the features from the rotated image. While in the RIFDS approach, the feature is extracted from small blocks from a single image by using MBLBP, PHT is applied for face recognition.        Fig. 20, the objectives of the paper have been achieved using RIFDS technique. The algorithm achieved promising comparable results. The accuracy is 99.99%. For the test images with angle starting from 30 • to 180 • results shows better performance than the said known algorithm and techniques.

Conclusions
This paper presents a new algorithm called Rotation Invariant Face Detection System (RIFDS) to detect the face from different angles of rotations. It aims to fast and accurately detect rotated faces by combining Polar Harmonic Transforms (PHTs) with Multi-Block LBP (MBLBP). In the RIFDS approach, texture patterns are extracted from the image using MBLBP, and PHT is used to keep invariant rotation characteristics. The proposed face detection system is able to detect faces within a short time and at different angles (i.e., 30 Firstly, if the scale of MBLBP is 3 × 3, it will not be able to acquire the primary features of a large scale. To solve this issue, the process is then generalized to used neighbor's information. The other is that when pp is used without Bessal Functions, not any other radial kernel can be defined explicitly, which causes some time increase the computational complexity if not defined properly. The technique is also tested for face detection at different image resolutions. It has been tested and verified that the proposed RIFDS technique can detect faces with different angles, facial expressions, and emotions speedily and accurately. The accuracy achieved is 99.99% as margin of .01% is due to noise and external uncontrollable factors like calculating ability of the algorithm as per significant figures of any numeric value. The extension or futuristic benefits of the algorithm can be used in the domain of automation, machine learning and deep learning through genetic algorithms for face detections from shadows. The application of the algorithm are in the areas of Twin face recognition, Object and shape recognition , Video or live surveillance, detection of face in the incarnation and in medical image processing for tumor detection by focusing the detection of malignant cells.