コンピュータ・サイエンス : ACM computing surveys

Audio- and video-based biometric person authentication

Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

/pdf/what-else-does-your-biometric-data-reveal-a-survey-on-soft-1d8sy6v8ai.pdf

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Thank you very much for reading handbook of biometrics. Maybe you have knowledge that, people have look numerous times for their favorite books like this handbook of biometrics, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their desktop computer. handbook of biometrics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the handbook of biometrics is universally compatible with any devices to read.

Handbook Of Biometrics

Thank you very much for reading advanced digital signal processing and noise reduction. Maybe you have knowledge that, people have search hundreds times for their chosen books like this advanced digital signal processing and noise reduction, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their laptop.

Advanced Digital Signal Processing And Noise Reduction

Biliary Atresia (BA) refers to a disease that mostly affects newborns by partially obstructing the bile ducts from the liver to the intestines, causing the trapped bile to damage the liver itself and often resulting in the need for a transplant. To detect BA, expert personell (e.g., pediatricians) or non-experts (e.g., the parents) usually analyze the color of the feces with the help of a reference stool color card. To automate this process, some approaches in the literature proposed smartphone apps enabling the parents to capture an image of the feces, select a point of the image to analyze, and compare it with the stool color card. However, such approaches consider only the local pixel chosen for matching and are therefore highly dependent on the position chosen by the user, who may choose a non-significant pixel to perform the analysis. In this work, we propose the first method in the literature for BA detection that considers a color-based segmentation and a nearest neighbor classification. Differently than the approaches in the literature, the color segmentation clusters the image in different areas based on the color and permits to automatically and robustly consider the corresponding cluster, and not only the local pixel, to perform the classification. Results on a database captured in uncontrolled conditions show the validity of the approach.

Biliary Atresia Detection Using Color Clustering and Nearest Neighbor Classification: A User Interactive Approach

Anomaly-Based Intrusion Detection System for DDoS Attack with Deep Learning Techniques

Image-to-image (I2I) translation models are widely employed in several fields, e.g., computer vision, security or medicine. Their goal is to map images from a source domain to a target domain while preserving content information. Despite their success, these models suffer from multiple weaknesses. For example, many practical scenarios do not consent to collect a sufficient amount of images, leading to imbalanced domains. Furthermore, mode collapse and training instability require a careful design and further discourage their deployment on edge devices. Finally, I2I models need an intensive computation to learn conditional probability distributions and are difficult to adapt to different contexts. These drawbacks mainly limit their large scale applicability. In this work, we want to shed light on the main solutions adopted to overcome the above issues and their impact on the performance. We also investigate several approaches to deploy these models on low-powered devices and weight sharing techniques to reduce the number of parameters and resources used.

Applications and Limits of Image-to-Image Translation Models

Deep-learning-based on convolutional neural networks (CNN) has been widely applied in synthetic aperture radar (SAR) target recognition and made significant progress. However, due to the physical effects of the equipment used to collect images, various degrees of speckle noise will be introduced into SAR images. Traditional CNN-based SAR target recognition methods are premised on the same noise intensity in the training and testing set, which is contrary to the target recognition in practice. To alleviate this problem, we propose a novel speckle noise resistant framework for SAR target recognition, called dual-consistency-alignment-based self-supervised learning. First, original SAR images are randomly added to speckle noise with different thresholds through multiplicative noise, after which contrastive pretraining is performed on unlabeled data. During this period, we combine instance pseudolabel consistency alignment and feature consistency alignment to align multiple threshold speckle noise views with original views under the same targets. Finally, the pretrained model is migrated to the downstream SAR speckle noise target recognition task. In this article, speckle noise modeling is conducted based on moving and stationary target capture and recognition data testing set, and experiment results reveal that this method can adapt to different intensities of speckle noise, is robust to modeled SAR image recognition, and maintains a high recognition rate even in small-sample learning.

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are increasingly considering Deep Learning (DL) due to its high accuracy in several fields, including medical imaging. In most cases, such methods use transfer learning techniques to compensate for the limited availability of labeled data. However, current methods for ALL detection use traditional transfer learning, which requires the models to be fully trained on the source domain, then fine-tuned on the target domain, with the drawback of possibly overfitting the source domain and reducing the generalization capability on the target domain. To overcome this drawback and increase the classification accuracy that can be obtained using transfer learning, in this paper we propose our method named “Deep Learning for Acute Lymphoblastic Leukemia” (DL4ALL), a novel multi-task learning DL model for ALL detection, trained using a cross-dataset transfer learning approach. The method adapts an existing model into a multi-task classification problem, then trains it using transfer learning procedures that consider both source and target databases at the same time, interleaving batches from the two domains even when they are significantly different. The proposed DL4ALL represents the first work in the literature using a multi-task cross-dataset transfer learning procedure for ALL detection. Results on a publicly-available ALL database confirm the validity of our approach, which achieves a higher accuracy in detecting ALL with respect to existing methods, even when not using manual labels for the source domain.

Angelo Genovese

Papers

Biliary Atresia Detection Using Color Clustering and Nearest Neighbor Classification: A User Interactive Approach

Anomaly-Based Intrusion Detection System for DDoS Attack with Deep Learning Techniques

Applications and Limits of Image-to-Image Translation Models

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection