feature detection are significant activities that influence ensuing assignments concentrated on the face, for example, gaze detection, coding, face acknowledgment, and demeanor, as well as understanding of gestures, face tracking, and so on and so forth.
Face landmarks like the nose tip, eye corners, end points of the eyebrow curves, jaw line, nostril corners, and ear projections can serve as anchor points on a face chart. A few Landmarks that are less influenced by expressions are more reliable can be termed as fiducially points. In imaging systems, fiducially points are treated as imprints intentionally positioned in the scene to function as a point of reference shown in Figure 1.2 [13].
Figure 1.2 Applications of Face-Land marking [4].
Applications of Face-Land Marking
• Expression Understanding: Facial expressions can be analyzed by means of temporal dynamics and spatial arrangements of landmarks.
• Face Tracking: Number of facial factors are depicted on the face graph model. Face tracking is acknowledged by letting the model chart advance as per shape of face parameters, facial segments, and their mathematical relations.
• Face Recognition: Locating the region of the eye and extracting holistic features from the windows fixated on different focal points.
1.3.4 Nearest Neighbor Approach
KNN Algorithm: A non-parametric regression and classification algorithm based on the model structure generated from data without any assumptions of its own.
KNN is used for measuring similarities by vector representation and comparison using an acceptable distance metric in various domains of data processing, pattern recognition, and intrusion detection. KNN is called memory-based or lazy learning in light of the fact that the manner in which it learns is simply storing representations of the training examples. An object is classified depending on the majority votes of its neighbors in the training set. The new model item will be ascribed to the class with its most comparable K-Closest Neighbors.
For facial acknowledgment, we can select the face descriptors and use the K-Nearest Neighbors (KNN) calculation to train our classifier.
Euclidean Distance Function of K-Nearest Neighbor can be used for feature extraction:
1.4 Related Work, A Review
P M Rubesh Anand (2018), along with other researchers, in his study, have done a complete analysis of the cloud environment and summed up the prevailing security threats in the cloud, conceivable outcomes, and alleviation in cloud administration with accentuation on access supervision, identity management, and services. Their research evaluates various facets with their commonly used techniques or mechanisms.
A K Jain (2012), in his research, sheds some light on the vulnerabilities of the biometric systems, the intrinsic limitations of the similarity of the any two biometrics, and its adversary effects. Unlike traditional authentication systems based on passwords, biometric authentications does not fully guarantee security.
A Patil (2018) in his paper discusses the security concerns of the cloud computing. Since cloud computing is a technology that delivers real time services, it is vulnerable to various kinds of data breaches. They discuss end to end communication through encryption, which would safeguard stolen information as the content would be encrypted and require security credentials [14].
To minimize, and ultimately beat, the dangers incorporated in the usage of customary strategies of authentication using PINs and passwords, a biometric framework for validation claimed to be more effective in controlling data breaches in cloud computing. In their research, they utilized an AES (Advanced Encryption Standard) algorithm for encrypting the data received from users at the time of enrollment and have devised another algorithm for the correlation of the user information with that of the layouts in the information database during authentication.
C S Vorugunti (2014) in his paper provides a simple and secure authentication system based on the SAAS model. It involves enrollment and verification as two steps of authentication. In the enrollment process, the biometric data is converted into a binary form. The feature extractor then converts the binary string into a set of features. In the verification process, the same feature will be processed when the user logins to the cloud. The process then verifies the cryptographic encryption and decryption operation on the users’ biometric data.
S. Ziyad (2014) proposes in a study an authentication that is secured by the amalgamation of biometrics and cryptography. Their system structure involves three phases, namely, the initialization phase, registration phase, and verification phase. At the time of registration, biometric data is obtained from the users, encrypted, and stored in a smart card. Each smart card contains an authentication number along with palm vein biometric data and other related information. During the verification phase, the data from the smart card is verified with template data in the database and if the data is verified and matched, then the connection with the server is established and the user can access the system [15].
Traditional systems apply the authentication process in one or various modes. A single-sign-on is a strategy that utilizes customary techniques for the user to access the system just a single time upon entering their identity, however, they can access different services at different levels. S. Bawaskar (2016), in her research paper, proposes an upgraded SSO based authentication framework based on a multi-factor concept. The authors suggest a continuous bit sequence of the oriented certificates utilizing greater management schemes. Accordingly, the framework is totally secured, taking into account the need of protection from malicious activities.
1.5 Proposed Work
Facial acknowledgment is a classification of biometric programming that maps a person’s facial features mathematically and stores the information as a face print.
Here, we propose a model for application in criminal justice systems. The model suggests the capacity to perform face identification in a group continuously or post-occasion, for open security, in urban communities, air terminals, at fringes, or other sensitive spots like religious congregations. It can help law enforcement agencies in better identification of possible suspects.
First of all, facial detection technique is used to confirm whether the image given as input is a face or not. On being detected as a face, facial marking is done using fiducially point analysis.
We will access the database from the cloud and the personal recognition will use the feature vectors from the training images to train or learn the classification algorithm. Data mining algorithms will be used for further processing and evaluation of data. Feature selection and feature extraction techniques can be used for improved accuracy shown in Figure 1.3.
Figure 1.3 System architecture.
