With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.

BACKGROUND: Protein methylation, a post-translational modification, is crucial in regulating various cellular functions. Arginine methylation is required to understand crucial biochemical activities and biological functions, like gene regulation, signal transduction, etc. However, some experimental methods, including Chip-Chip, mass spectrometry, and methylation-specific antibodies, exist for the prediction of methylated proteins. These experimental methods are expensive and tedious. As a result, computational methods based on machine learning play an efficient role in predicting arginine methylation sites. RESULTS: In this research, a novel method called PRMxAI has been proposed to predict arginine methylation sites. The proposed PRMxAI extract sequence-based features, such as dipeptide composition, physicochemical properties, amino acid composition, and information theory-based features (Arimoto, Havrda-Charvat, Renyi, and Shannon entropy), to represent the protein sequences into numerical format. Various machine learning algorithms are implemented to select the better classifier, such as Decision trees, Naive Bayes, Random Forest, Support vector machines, and K-nearest neighbors. The random forest algorithm is selected as the underlying classifier for the PRMxAI model. The performance of PRMxAI is evaluated by employing 10-fold cross-validation, and it yields 87.17% and 90.40% accuracy on mono-methylarginine and di-methylarginine data sets, respectively. This research also examines the impact of various features on both data sets using explainable artificial intelligence. CONCLUSIONS: The proposed PRMxAI shows the effectiveness of the features for predicting arginine methylation sites. Additionally, the SHapley Additive exPlanation method is used to interpret the predictive mechanism of the proposed model. The results indicate that the proposed PRMxAI model outperforms other state-of-the-art predictors.

A multimodal biometric recognition system on Internet of Things (IoT) with Blockchain environments has been proposed in this article. This system distributes a decentralized biometric authentication process mechanism and improves security in the IoT environment. The implementation of this system consists of five components: 1) image preprocessing; 2) feature representation; 3) cancelable biometrics; 4) classification; and 5) encryption–decryption of multimodal biometrics templates. A region of interest is segmented from each biometric trait during image preprocessing. Then, a discriminant feature extraction technique has been employed for feature computation. A cancellable biometric system (CBS) is introduced to secure and preserve the original biometric features from external hazards and misuse. The extracted cancelable features undergo classification to perform the subjects’ authentication. Then, a method of encryption–decryption of templates is performed to handle the various online authentication attacks and improve IoT-enabled authentication. Finally, the recognition scores due to iris, periocular, palmprint, and face biometrics are fused to increase the performance of the proposed IoT-enabled multimodal biometric system. The proposed system obtains identification performance 99.92%, 100% for CASIA-V4-distance (CASIA-DIST), UBIRIS-v2 iris, 100% for periocular (CASIA-DIST, UBIRIS-v2), 100% for Bosphorus palmprint, and 100% for FERET face databases using 30-D cancelable features that show the superiority of the proposed system as compared with state-of-the-art methods.

This paper presents a secure face recognition system with advanced template protection schemes for Cyber-Physical-Social Services (CPSS). The implementation of the proposed system consists of five components. The initial step performs image preprocessing, where it detects the facial region from the captured image using the Tree-Structured Part Model (TSPM). The second phase involves feature extraction, where it utilizes the Scale Invariant Feature Transform (SIFT) descriptor to extract features from small patches of the preprocessed images, forming a collection of feature descriptors. The collection of feature descriptors is then clustered using the K-means clustering algorithm, returning the centers of K-clusters that serve as the vocabulary of a dictionary. Finally, a histogram is generated using the vocabularies and frequencies, referred to as the “Bag of Visual Words (BoVW)”. Using this dictionary and a feature learning technique called Sparse Representation Coding (SRC), followed by Spatial Pyramid Mapping (SPM), the system generates feature vectors from training/testing image samples. In the third component, the modified FaceHashing technique is applied to the original feature vectors, generating cancelable feature vectors. The fourth component employs a Bio-Cryptographic technique to preserve the cancelable feature vectors in a database. Lastly, the fifth component utilizes a multi-class linear SVM classifier on the decrypted and query-cancellable feature vector to classify users. The system evaluates its performance using FERET and CASIA-FaceV5 benchmark databases, providing 100% identification accuracy for 200-dimensional cancelable feature vectors. The performance and security comparisons demonstrate the superiority of the proposed system over existing methods.

In Healthcare, the Internet of Things (IoT)-enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this article, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three-step template protection techniques are proposed: (i) Cancelable biometrics , (ii) BioCrypto-Circuit , and (iii) BioCrypto-Protection . The performance of the proposed system has been tested on four benchmark face databases, CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.

Abstract A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the ‘Kaggle’ website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

Essential genes are considered to be the genes required to sustain life of different organisms. These genes encode proteins that maintain central metabolism, DNA replications, translation of genes, and basic cellular structure, and mediate the transport process within and out of the cell. The identification of essential genes is one of the essential problems in computational genomics. In this present study, to discriminate essential genes from other genes from a non-biologists perspective, the purine and pyrimidine distribution over the essential genes of four exemplary species, namely Homo sapiens , Arabidopsis thaliana , Drosophila melanogaster , and Danio rerio are thoroughly experimented using some quantitative methods. Moreover, the Indigent classification method has also been deployed for classification on the essential genes of the said species. Based on Shannon entropy, fractal dimension, Hurst exponent, and purine and pyrimidine bases distribution, 10 different clusters have been generated for the essential genes of the four species. Some proximity results are also reported herewith for the clusters of the essential genes.