Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification, which helps prevent infections like COVID-19 from spreading. Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses, replayed the video, and print attacks. The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness. Seven assorted feature creation ways are studied in the presented solutions, and these created features are explored for the training of eight distinct machine learning classifiers and ensembles. The predicted iris liveness identification variants are evaluated using recall, F-measure, precision, accuracy, APCER, BPCER, and ACER. Three standard datasets were used in the investigation. The main contribution of our study is achieving a good accuracy of 99.18% with a smaller feature vector. The fragmental coefficients of Haar transformed iris image of size 8 ∗ 8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size (64 features). Random forest gave 99.18% accuracy. Additionally, conduct an extensive experiment on cross datasets for detailed analysis. The results of our experiments show that the iris biometric template is decreased in size to make the proposed framework suitable for algorithmic verification in real-time environments and settings. © 2023 Tech Science Press. All rights reserved.

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

Facial expressions are mirrors of human thoughts and feelings. It provides a wealth of social cues to the viewer, including the focus of attention, intention, motivation, and emotion. It is regarded as a potent tool of silent communication. Analysis of these expressions gives a significantly more profound insight into human behavior. AI-based Facial Expression Recognition (FER) has become one of the crucial research topics in recent years, with applications in dynamic analysis, pattern recognition, interpersonal interaction, mental health monitoring, and many more. However, with the global push towards online platforms due to the Covid-19 pandemic, there has been a pressing need to innovate and offer a new FER analysis framework with the increasing visual data generated by videos and photographs.Furthermore, the emotion-wise facial expressions of kids, adults, and senior citizens vary, which must also be considered in the FER research. Lots of research work has been done in this area. However, it lacks a comprehensive overview of the literature that showcases the past work done and provides the aligned future directions. In this paper, the authors have provided a comprehensive evaluation of AI-based FER methodologies, including datasets, feature extraction techniques, algorithms, and the recent breakthroughs with their applications in facial expression identification. To the best of the author's knowledge, this is the only review paper stating all aspects of FER for various age brackets and would significantly impact the research community in the coming years.

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris- PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM CRF CNB, SVM CRF CRT, and RF CSVM CMLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 x 4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements.