Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach.

Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method's performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies.

Attention-based deep learning framework to recognize diabetes disease from cellular retinal images.

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary cause of DR in patients is high blood sugar, and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and their automation applications can recognize the presence of DR in retinal cells and vessel images. Authors have proposed an attention-based hybrid model to recognize diabetes in early stage to prevent harmful clauses. Proposed methodology uses DenseNet121 architecture for convolution learning and then, the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates binary and multiclass classification to recognize the infection and the spreading of disease. Binary classification recognizes DR images either positive or negative, while multiclass classification represents an infection on a scale of 0-4. Simulation of the proposed methodology has achieved 98.57% and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention-based deep learning model has achieved remarkable accuracy to detect diabetic infection from retinal cellular images.