Intruder Identification Using Footprint Recognition with PCA and SVM Classifiers

Footprint recognition plays an important role in criminal investigation, security protection and other fields. In terms of the key problems of feature extraction and classification, a footprint recognition algorithm is proposed based on optical footprint images. The algorithm first transforms the footprint image into the complex frequency domain through the dual-tree complex wavelet transform (DTCWT), then extracts the histogram of oriented gradient (HOG) features of the footprint image in the complex frequency domain. Finally, a symmetric projection matrix is obtained through metric learning. The symmetric projection matrix is used to project the original features to a new feature space. And then the SVM is used to identify the optical footprint in the new feature space. The experimental results show that on the optical footprint images of 134 people, the recognition accuracy of the algorithm in this paper is improved, which is valid to help the application and research of footprints.

Facial recognition methods were first explored in security systems to identify and compare human faces and is far superior compared to biometric and iris recognition, this technique has been implemented in iris recognition, image detection etc. Recently these methods have been explored in other fields of study and have become a commercial identification and marketing tool. This paper describes the different algorithms of facial recognition and compared their recognition accuracies. The face is detected through Haar Cascades algorithm which is saved into a database, after that, the study intended to compare facial recognition accuracy of the well-known algorithms Eigen faces with PCA, SVM, KNN, and CNN. The results showed out of the three algorithms we used CNN yielded the maximum accuracy.

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

The principle component analysis(PCA) is a common feature extraction method in machine learning and pattern recognition approaches. PCA has been used in many applications, and face recognition in which specific faces are recognizing in an images database is one of the popular applications. The default distance metric which has been used with PCA based-face recognition is Euclidean distance. In this study, we have tested the Mahalanobis distance instead of Euclidean, and PCA based on Mahalanobis distance suggested a better performance on our students images database with highest recognition rate. However, we proposed weighted and normalized Mahalanobis distance based PCA-face recognition(PCA_WNMD). The proposed algorithm (PCA_WNMD) showed an improvement in faces recognition rate when tested on our students images database compared to PCA based on Mahalanobis and default Euclidean distances.

In today’s world many times need the human identification for the purpose of security of information, authentication, search criminals, find missing child. The biometric recognition uses for human identification because the biometrical characteristics are unique, and it’s based on two types are behavioural and physiological characteristics. This paper include biometric recognition system overview fingerprint recognition, face recognition, iris recognition, speech recognition and gesture recognition. overview include the various types of algorithm using in biometric recognition system are discrete cosine transform, key local variation, minutiae matching algorithm, PCA, active contour algorithm, fuzzy cmeans clustering, LPCC Algorithm, LDA Algorithm and Fuzzy Neural Network. The standard techniques implement following some of algorithms and recognition rate is near prediction about 100%. Keywords: Biometric Recognition, LDA, PCA Algorithms, Database.

<p><em>The aliveness of a person can be related with the heart beat rhythm. So the heart beat rhythm can be signalized into an ECG. Nowadays for the biometric authentication purpose we normally use the finger print, face &iris recognition etc. Among that ECG is more against forgeries and it is advanced technique than finger print &face recognition. This paper describes different methods used in identifying a person using Electrocardiogram signals. The same signal used for the classification of emotions also. User identification is the common method for identify a user in the business field and other organizations.  User ID is one of the most common authentication system used within computing systems, networks, applications and over the Internet. Commonly used authentications are fingerprint, face recognition, iris recognition & RFID etc. Among ECG is universal, continuous& difficult to falsify. So the user identification using ECG is the new trend in biometric authentication. ECG is a periodical signal creates P,QRS,T through depolarization of atrium, repolarization &depolarization of ventricle. ECG is a bio signal & is unique because the variation in amplitude of R wave and the time duration of each wave is different for each individual. There are many different types of </em><a href="https://www.verywellmind.com/what-are-emotions-2795178"><em>emotions</em></a><em> that have an influence on how we live on earth and how we behave to others. The emotions are identified were happiness, sadness, disgust, fear, surprise, and anger. This paper describes the different method used to identify the person &classifies the emotion using ECG.</em></p>

Infrared (IR) Face recognition has become an area of growing interest since it can operate in dim light and total darkness. This paper introduces a comparative study between the infrared face recognition systems using the principle component analysis (PCA) and the Zernike moments (ZM) techniques. The performance is evaluated according to the recognition rate, time consumption, and immunity to both Salt & Pepper and Gaussian noise. The analysis shows that the PCA technique has the same performance as the ZM technique if a large size dataset is used. On the other hand, the ZM technique outperforms the PCA technique when using a small size dataset, but it consumes a time approximately equal to four times that required by the PCA technique. The simulations also show that the ZM technique outperforms the PCA technique in the presence of both Salt & Pepper and Gaussian Noise types at various values of noise variance.

Personal identification based on biometrics technology is a trend in the future. Traditional approaches, for example, keys, ID cards, username and password, are neither satisfactory nor reliable enough in many security fields, biometrics authorizations based on face, iris, fingerprint have become a hot research filed. In those methods, iris recognition is regarded as a high accuracy verification technology, so that many countries have the same idea of adopting iris recognition to improve the safety of their key departments. The human iris can also be considered a valid biometrics for personal identification [Richard P W, 1996]. Biometrics recognition based on iris patterns is a hotspot as face recognition and fingerprint recognition recently years. The iris is the colored ring on the human eye between the pupil and the white sclera. Lots of physical biometric can be found in the colored ring of tissue that surrounds the pupil, such as corona, crypts, filaments, flecks, pits, radial furrows and striations. The iris features can be encoded by mathematical representation so that the patterns can be compared easily. In real-time iris recognition application system, iris localization is a very important step for iris recognition. The iris regions segmentation accuracy and localization real-time performance will affect the whole recognition system’s correct rate and effectiveness for large-scale database. Because iris region is a small object and has low grey value, it is very difficult to capture high contrast iris image clearly. In order to improve iris image contrast, usually some illuminations such as near infrared light source are used to increase intensity; however these illuminations may result in some faculas in iris image and affect iris segmentation and iris features. Here, we will discuss iris recognition system’s algorithm, all steps of iris recognition system will be introduced in details. Finally, we will show the experimental results based on iris database.

Face recognition is one of the fastest-growing technology in the field of biometrics, electronic business (e-business), and image processing. With the increase of e-business, the lack of transaction security has led to the increase in fraudulent activities. Most systems such as e-business transactions require a reliable recognition system to provide services to authentic and legitimate users. Biometric recognition using facial traits is the solution to dealing with the security issues in e-business transactions. Biometric recognition is the process of digitally recognizing a person based on their physiological or behavioral traits. Face recognition technology is applied in the fields like e-business, home security, and criminal identification. This paper proposes the implementation of the face recognition system for login in e-business transactions. A three-phase enhanced e-business security system that consists of face detection, feature extraction, and face recognition is designed to ensure that the security in e-business transactions is always maintained. The biometric face recognition system will reduce the transaction security issues in e-business. The python OpenCV library was used to simulate and evaluate the effectiveness of the face recognition system. Experimental results are provided to illustrate the effectiveness of the face recognition system.

Iris recognition method is the most significant biometric modality for human identification. Currently, multiple deep structured architectures have been employed in biometric recognition with various features like robust generalization ability, high accuracy, and automatic learning. It is also considered as a more emerging area in biometrics along with machine learning approaches. Deep CNN is a conventional approach of image processing broadly utilized in several fields, however, it is simply influenced by slight disturbances and also includes bad anti-noise capacity in image classification. The major scope of this work is to implement adaptive deep learning-aided iris recognition. The key contribution of this paper relies on enhancing iris segmentation and iris recognition. Initially, the iris images are pre-processed, which are subjected to segmentation. The segmentation approach focuses on a Hough transform with K-Means Clustering (HT-KMC). Once the segmentation of the iris is done, the “Enhanced Faster Region-Convolutional Neural Network (E-Faster-RCNN)” is developed for the recognition purpose. The number of suitably hidden neurons of the conventional Faster-RCNN is optimized by the Adaptive-Sail Fish Optimization (ASFO) algorithm to obtain the highest efficiency. The simulation findings of the recommended approach proved that the method is effective in iris recognition by experimenting on a benchmark dataset.

A need objective of the utilization of biometrics is to give character confirmation or the capacity to precisely perceive people. Biometric frameworks have fundamentally enhanced individual recognizable proof and confirmation, assuming a critical part in individual, national and worldwide security. Three biometric recognition are utilized as a part of this project. Face recognition, Fingerprint recognition, Iris recognition is done in this project. Daugman's calculation is utilized for iris recognition, Euclidean distance calculation is utilized for face, fingerprint recognition. The proposed approach displays high level of precision. The database of authorized and unauthorized is used in this project. This project is software based.

The objective of the paper is face recognition using PCA and Bit plane slicing. It made a study on the dimensionality reduction on bit plane of images for face recognition. The proposed frame work would aid in robust design of face recognition system and addressed the challenging issues like pose and expression variation on ORL face database. It is in contrast to PCA on the image the design of PCA on bit plane reduces computation complexity and also reduces time. In the proposed frame work image is decomposed with the help of bit plane slicing, the feature have been extracted from the principle component analysis (PCA).

The human face exhibits a high level of complexity when it is regarded as a multidimensional visual model, leading to face recognition systems that require difficult and extensive computations for coding and decoding the face images. A well-established approach in this regard is based on using principle component analysis (PCA) for both feature extraction and face recognition, known as the eigenface approach. This technique, despite a good recognition rate, suffers from the disadvantage of high computation cost due to the complexity of the PCA algorithm. In this paper, we use a geometrical approximated PCA (gaPCA) algorithm for computing the eigenfaces for three different datasets. The face recognition task is performed using a similarity score based on the inverse Euclidean distance for the first two datasets and using a nerual network in the third case. All the results are compared to the case where standard PCA is used. These accuracy results show that gaPCA represents a viable alternative to the classical statistical approach for computing the principal components.

This paper presents a combination techniques of appearance-based and feature-based feature extraction on the T-Zone face area to improve the recognition performance. This study shows that the T-Zone area and the combined technique provides a significant impact on the face recognition rate. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. High recognition rates are obtained when compared to other face recognition methods of the same class. Our result shows of 7% improvement compared with PCA and 2% improvement compare with Sub-Holistic PCA. Our studies proves that the T-Zone area which is consisting of eyes and nose region is a ‘significant facial region’, and we also show that LBP can easily be combined with PCA to reduce the length of the feature vector, while the recognition performance is improved.

Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are known as classical techniques used in face recognition. Both 2-D PCA, 2-D LDA are based on 2-D matrices as opposed to classical PCA and LDA which are based on 1-D vectors. In current work Discriminative Component Analysis(DCA) simultaneous projection of probe image into PCA and LDA face spaces for face recognition is proposed. Also Alternate DCA is proposed. The study is conducted on AT&T database (formerly ORL database) and study reports are encouraging as that of other analysis.