Proposed SIFT with LM-NN of different plastic surgery faces.
Abstract
This chapter presents a new technique called entropy volume-based scale-invariant feature transform for correct face recognition post cosmetic surgery. The comparable features taken are the key points and volume of the Difference of Gaussian (DOG) structure for those points the information rate is confirmed. The information extracted has a minimum effect on uncertain changes in the face since the entropy is the higher-order statistical feature. Then the extracted corresponding entropy volume-based scale-invariant feature transform features are applied and provided to the support vector machine for classification. The normal scale-invariant feature transform feature extracts the key points based on dissimilarity which is also known as the contrast of the image, and the volume-based scale-invariant feature transform (V-SIFT) feature extracts the key points based on the volume of the structure. However, the EV-SIFT method provides both the contrast and volume information. Thus, EV-SIFT provides better performance when compared with principal component analysis (PCA), normal scale-invariant feature transform (SIFT), and V-SIFT-based feature extraction. Since it is well known that the artificial neural network (ANN) with Levenberg-Marquardt (LM) is a powerful computation tool for accurate classification, it is further used in this technique for better classification results.
Keywords
- face recognition
- plastic surgery
- scale-invariant feature transform
- (SIFT) feature
- EV-SIFT feature
- Levenberg-Marquardt-based neural network classifier (LM-NN)
1. Introduction
Human faces are multidimensional and complex visual stimuli, which contain useful information about the uniqueness of a person. Recognizing their faces used for security and authentication purposes has taken a new turn in the current era of computer image and vision analysis, for example, in monitoring applications, image recovery, man-machine interaction, and biometric authentication. Normally, the facial recognition system does not have the sense of touch or human interaction to complete the recognition process. This is one of the benefits of face recognition in relation to other recognition methods. Facial recognition can designate the verification phase [1] or the identification phase [2]. In the verification phase, the correspondence between two faces is resolved. There are many methods available to achieve facial recognition [3, 4, 5, 6, 7, 8]. But the accuracy of recognition is not always high. This is due to variations in lighting levels, facial expressions, poses, aging, low-resolution input images, or facial markings [9, 10]. Several investigators have implemented several methods of face recognition to treat the effects of imposition [11] of illumination [12], low resolution [13], aging [14], or a combination thereof [15]. However, these uncertainties could be overcome, and, in the face of plastic surgery, recognition will intensify with the identification of the person. The fact that face recognition in plastic surgery is due to the lack or variation of facial components, the texture of the skin, the general appearance of the face, and the geometric relationship between facial features or variation of the facial components [16, 17, 18]. Plastic surgery, both economic and sophisticated, has attracted people from all over the world. However, only a few contributions or research methodologies have been reported in the literature to address the problem of face recognition of plastic surgery. Few of them include recognition by local region analysis [19], a local form of cascade texture function (SLBT) with periocular features [20]. A review was also carried out in [21] to illustrate the use of multimodal features in the recognition of plastic surgery on the basis of contributions.
1.1 Related works
De Marsico et al. [22] have made perfect recognition of the face, undergone cosmetic surgery, with region-based approach on a multimodal supervised architecture, also named as Split Face Architecture (SFA). Author proved dominance of their method by the application of supervised SFA to conventional PCA as well as FDA, toward LBP in the multiscale, rotation-invariant version with uniform patterns, face analysis for commercial entities (FACE), as well as face recognition against occlusions and expression variations (FARO).
Kohli et al. [23] enclose layout of multiple projective dictionary learning framework (MPDL) that never needs to figure norms to recognize usual faces, which have undergone modification via cosmetic surgery. Several projective dictionaries as well as compact binary face descriptors have been used to understand local and global plastic surgery face representations, in order to facilitate the distinction between plastic surgery faces and their original faces. The tests performed on the plastic surgery database resulted in an accuracy of about 97.96%.
Chude-Olisah et al. [24] has overcome the degradation of facial recognition performance; they have found that the approach had gone beyond the facial recognition approaches of cosmetic surgery before accessible, regardless of changes in lighting, facial expressions, and other changes resulting from cosmetic surgery. Ouanan [25] has introduced HOG feature-based facial recognition approach, which uses HOG as a substitute of DOG in the scale-invariant feature transform. Ouloul [26] introduces a perfect recognition approach for face using SIFT feature in RGBD images which depend on RGBD images produced by Kinect; this kind of cameras are low price, as well as it can be utilized in every setting and in several situations. Bhatt et al. [27] have proposed a multi-objective granular evolutionary method, which provides the pairing of images taken before and after in cosmetic surgery. Primarily, the algorithm generates superimposed face granules in three levels of granularity. Facial recognition in plastic surgery has undergone several developments in recent years. Contributions to the research were reported in the literature, either in the feature extraction phase, in the classification phase, or in both phases.
2. Granular approach for recognizing surgically altered face images using EV-SIFT and LM trained NN
The surgical face recognition is developed, which is based on the granular approach and Laplacian sharpening since it is identified that the sharpening of images will automatically enhance the cornerness and contrast of the image granules. Further, the key point elimination is done in this technique with entropy threshold, because entropy is the effective selection criterion that is used to eliminate the unreliable interest points. Since it is well known that the artificial neural network (ANN) with Levenberg-Marquardt (LM) is a powerful computation tool for accurate classification, it is further used in this technique for better classification results. The architecture diagram of the proposed face recognition technique is diagrammatically illustrated in Figure 1.
The testing image
3. Preprocessing: granular and Laplacian sharpening
This is the initial process with the input image
3.1 Preprocessing-I
The image
Positive Laplacian operator
Negative Laplacian operator
Moreover, one of the differences among the operators is that Laplacian will not use any corresponding direction. However, it uses edges in two classifications:
Inward edges
Outward edges
3.2 Preprocessing II
This is the foremost process of the developed model. Consider
4. EV-SIFT, local binary pattern (LBP), and center-symmetric local binary pattern (CSLBP)
4.1 EV-SIFT
Consider the face image
In Eq. (1),
4.1.1 Acquisition of the EV-SIFT key points
Choosing the key points in the variation of the Gaussian function is the vital role to be considered. The parameters of the key point are purely depending on distribution property of the gradient operation of the image. Thus, the formulation of both the orientation and gradient modules is done, which registers the invariance toward the rotation of the image. The computation of orientation and gradient module is defined in Eqs. (4) and (5), where
The scales used by
4.1.2 Entropy-based feature descriptor
The Changeable information is measured using entropy. It basically defines the statistical measure of randomness, which determines the texture of the input image. Only the least effect remains in the higher-order statistical feature due to the entropy on uncertain deviations in the face. The following steps show the entropy-based feature descriptor:
The level of Gaussian blur of the image is selected by orientation and gradient magnitude with entropy, and also the volume of the image is also sampled in terms of scale of key points at particular key point location. The sample is an 8 × 8 neighbor window, which is centered on the key point and splits the neighbor into 4 × 4 child window. Hence, the formulation of gradient orientation histogram is done along with eight bins with the aid of each child window. In such a way that within each key point, each descriptor intends the 4 × 4 array of histograms that comprises eight bins. The feature vector attained is the size of 4 × 4 × 8 = 128 dimension.
4.2 Local binary pattern (LBP)
LBP [1] operator is designed for texture description. It encodes the pixel-wise data in texture images, in such a way that a label is assigned to every pixel of the image. This is done by thresholding the 3 × 3 neighborhood of all pixel value with the center pixel, and the result must be a binary number. The basic LBP thresholding function
Figure 4 illustrates the sample of attaining an LBP micro pattern when the threshold is set to 0. Further, the resultant histogram of the micro pattern presents the data related to the distribution of edges, spots, and more local features that present in the image. It is observed that the LBP is a great tool for face recognition. Despite a number of static learning approaches that tune with more parameters, LBP is more effective since it has an “easy-to-formulate” feature extraction process, and also the matching strategy is also very simple.
4.3 Center-symmetric local binary pattern (CSLBP)
CSLBP [1] is established for interest region description. It purposes for least LBP labels to generate smaller histograms, which are well suited to utilize in region descriptors. Moreover, it is designed for better stability, especially in regions that include the face image. Here, the comparison of pixel values are not done between the pixels and center pixels; rather the opposing pixels are symmetrically compared in correspondence to the center pixel, which is defined in Eq. (10):
where
In this work, the value of
5. Recognition system: Levenberg-Marquardt-based neural network classifier (LM-NN)
In this work, LM-NN classifier is used for recognition purpose. The NN model is represented in Eqs. (11)–(13), where
where
Here, the LM algorithm is used for training the NN model. The error function
where
where
If trail
Back to step 2
else
Back to step 4
End if
6. Results and discussion
6.1 Experimental setup
The cosmetic surgery face recognition experimentation is conducted in MATLAB 2015a. The database including presurgery faces and postsurgery faces are downloaded from http://www.locateadoc.com/pictures/. The experimentation is performed for different plastic surgery faces. The total number of plastic surgery faces in the database is 460, where it comprises 68 images from blepharoplasty (eyelid surgery), 51 images from brow lift (forehead surgery), 51 images from liposhaving (facial sculpturing), 17 images from malar augmentation (cheek implant), 18 images from mentoplasty (chin surgery), 54 images from otoplasty (ear surgery), 75 images from rhinoplasty (nose surgery), 74 images from rhytidectomy (facelift), and 52 images from skin peeling (skin resurfacing).
6.2 Granularity preprocessing
By dividing the face image into varied regions, we get the vertical as well as horizontal face granules as illustrated in Figure 6. The horizontal granules are represented as R1, R2, and R3, and the size is 150 × 150/3. Similarly, the vertical granules are denoted as R4, R5, and R6, which is of 150/3 × 150 size.
6.3 Analysis on EV-SIFT
In this work, EV-SIFT descriptor is used for the feature extraction. Figure 7 illustrates the original images. For each original image, the corresponding vertical edge and horizontal edge of the image were evaluated, and it is illustrated in Figures 8 and 9. The gradient magnitude of the images is also shown in Figure 10. Similarly, the theta images of the given input images are illustrated in Figure 11.
One of the important processes is the evaluation of image orientation of the eight angles such as
6.4 Learning performance of LM-NN
The performance of the LM-NN classifier is illustrated in Figure 18. It is observed that the best performance of the classifier is attained at the epoch 7, where the training performance is 0.00022204, gradient is 7.0363e-08, Mu is 1e-10, and the validation fail is 0 since there is no validation attained.
6.5 Comparative performance analysis of best-performing methods of proposed approaches
While analyzing the first research technique, in the evaluation on LM-NN, it is observed that the EV-SIFT proposed technique attained better results in all the measures like accuracy, sensitivity, specificity, precision, false-positive rate (FPR), false-negative rate (FNR), net present value (NPV), false discovery rate (FDR), and F1score (also F-score or F-measure) which is a measure of a test’s accuracy and Matthews correlation coefficient (MCC), respectively. The evaluation is summarized in Tables 1–3.
LM-NN | ||
---|---|---|
Accuracy | Rhinoplasty | 0.92 |
Sensitivity | Malar augmentation | 0.24 |
Specificity | Rhinoplasty | 0.97 |
Precision | Skin peeling | 0.04 |
FPR | Rhinoplasty | 0.03 |
FNR | Malar augmentation | 0.76 |
NPV | Rhinoplasty | 0.97 |
FDR | Skin peeling | 0.96 |
F1score | Skin peeling | 0.06 |
MCC | Skin peeling | 0.04 |
LM-NN | ||
---|---|---|
Measures | Surgery | Attained result |
Accuracy | Rhytidectomy | 0.93 |
Sensitivity | Mentoplasty | 0.19 |
Specificity | Rhytidectomy | 0.97 |
Precision | Skin peeling | 0.03 |
FPR | Rhytidectomy | 0.03 |
FNR | Mentoplasty | 0.81 |
NPV | Rhytidectomy | 0.97 |
FDR | Skin peeling | 0.97 |
F1score | Skin peeling | 0.05 |
MCC | Skin peeling | 0.03 |
LM-NN | ||
---|---|---|
Accuracy | Rhinoplasty | 0.984 |
Sensitivity | Brow lift | 0.17 |
Specificity | Rhinoplasty | 0.97 |
Precision | Skin peeling | 0.04 |
FPR | Rhinoplasty | 0.03 |
FNR | Malar augmentation | 0.83 |
NPV | Rhinoplasty | 0.97 |
FDR | Skin peeling | 0.96 |
F1score | Skin peeling | 0.06 |
MCC | Skin peeling | 0.04 |
It is observed that the proposed V-SIFT with LM-NN has achieved more over the conventional methods for various plastic surgeries, which is summarized in Table 2. It is observed that for all the measures, the method has attained better results, which also leads to the other types of plastic surgery.
From the second technique, it is observed that the proposed EV-SIFT with LM-NN are achieved more over the conventional methods for various plastic surgeries, which is summarized in Table 3. It is observed that for all the measures, the method has attained better results.
7. Conclusions
This chapter gives the detailed description of the second research technique. The feature descriptor EV-SIFT that is used for feature extraction is well explained. Further, the LM-based NN classifier is defined in this chapter, and the performance of both the EV-SIFT and LM-NN classifiers is shown in the Result section. The better work of EV-SIFT is effectively demonstrated in this section, which shows how the images are distinguished between them. The analysis of the LM-NN classifier is also more satisfactory with better performance.
Acknowledgments
To begin with, I express gratitude to God Shri Gajanan Maharaj, who provided me the potency as well as the ability to bring out this research. Additionally, I express my gratefulness to supervisor, Prof. S.N. Talbar, for his inspiration, priceless suggestion, and support along with concentration during exploration of research. I express thanks to every friend for giving out their experience and information. I also want to give an exceptional thanks to my companion for his honest advice and steady support to do a high-quality research.
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