Italy

In this work we use two approaches to prevent double spend-
ing and secure IoT transaction through Blockchain and information fu-
sion techniques: the rst method is based on Smart Contracts; while
the second method is implemented by a novel Blockchain system. Both
methods use a hybrid RSA and biometric codes fusion in order to have
an encrypted, taking account of the privacy, identity of the spender. In
Blockchain a contract between the parties is stipulated and the biomet-
ric fused code are kept. The used coin pack is tagged, so a future double
spending or any illegal action on the IoT network or Smart city becomes
forbidden and prosecutable legally. The second part of the work will
present a novel Blockchain where a transaction can be signed only if the
user is successfully authenticated though his biometric fused key.

ICPR 2020

Blockchain-based iris authentication in order tosecure IoT access and digital money spending

## Dr. Nishikant (Nish) Sonwalkar: Academician, Scientist, Musician

![](https://assets.underline.io/uploads/markdown_image/1/image/0f9b63a0b3deba4a355d98e2c87af68a.png)

Dr. Sonwalkar is regarded as a leading expert in the application of computers in education. He has extensive research and development experience in hypermedia authoring, mobile learning, adaptive learning, and brain-based synaptic learning, advanced scientific visualization, database management systems, and computer simulation.

After receiving doctoral degree from MIT in the area of Molecular Dynamics of Nano-interfaces, he joined MIT as faculty in Mechanical Engineering, but his passion for technology enabled education led him become the founding director of Hypermedia Teaching Facility (Hypermedia Lab) at MIT and later he was appointed as the Principal Educational Architect of MIT.

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NISH 2021

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1st generation such as UADFV and FaceForensics++ up to the latest databases of the 2nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This
study provides an exhaustive analysis of both 1st and 2nd DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specic facial regions as input to the fake detection system.
Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2nd generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors.

DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

This paper proposes an ensemble of different state-of-art algorithms for realizing a face search system aimed at achieving higher
accuracies compared to any single algorithm. This is achieved by leveraging the most promising deep networks (Facenet, OpenFace, DeepFace, and
VGGFace – originally trained for face recognition) and different Approximate Nearest Neighbor Search (ANNS) algorithms (Annoy and LSHash).
Face images in the database are subjected to feature extraction (embeddings computed by deep networks) and indexing (in set structure for
faster search) by ANNS algorithms. An input face query image is processed in the following four stages. First, the face region is detected from
the query image and appropriately aligned for further processing. Second,
the facial features are extracted using multiple deep networks. Third, the
ANNS algorithms perform a fast search by efficiently shrinking the gallery
size from millions to a few hundred faces. Fourth, a fine matching is performed using two different methods separately to produce the final search
results. These are (a) cosine similarity and (b) score-based matching and
re-ranking of results. The experimental results demonstrate the diversity in results obtained by the use of multiple feature extractors and ANNS
techniques and the accuracy achieved by using the proposed ensemble
framework.

A Novel Ensemble Framework for Face Search

Food texture is a complex property; various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.

Recognition of food-texture attributes using an in-ear microphone

A suitable combination of data in a multimodal emotion recognition model allows conveying and combining each channel's information to achieve a better recognition of the encoded emotion than would be possible using only a single modality and channel. In this paper, we focus on combining speech and physiological signals to predict the arousal and valence levels of the emotional states of a person. We designed a neural network that can use the information from raw audio signals, electrocardiograms, heart rate variability, electro-dermal activity, and skin conductance levels, to predict emotional states. The proposed deep neural network architecture works as an end-to-end process, which means, neither any pre-processing of the input data nor post-processing of the prediction of the network was applied. Using the data of the modalities available in the publicly accessible part of the RECOLA database, we achieved results comparable to other state-of-the-art approaches.

MMultimodal Emotion Recognition Based on Speech and Physiological Signals Using Deep Neural Networks

Semi-supervised learning exploits unlabeled data to improve generalization ability with insufficient annotations. In recent years, Mean Teacher method(MT) obtained impressive performance using prediction consistency as regularization. However, severe ambiguity in medical images makes the targets in the teacher model highly unreliable in obscure regions, thereby limits the model capability. To address this problem, we propose a novel multi-task learning semi-supervised framework to gain hierarchical consistency through training process. Specifically, we introduce region and shape predictions as subtasks to obtain the coarse-grained location and fine-grained boundary information. Then we predict pixel-level segmentation by fusing the hierarchical feature. Since calculating consistency loss in more loose regions typically alleviates the degradation caused by learning from unreliable targets, our teacher model generate guidance from each of the subtasks. Moreover, we focus on the geometrical correlations in different tasks and proposed the constraint method to refine the segmentation for accurate guidance. Experiments on the left atrium segmentation dataset show our algorithm achieves state-of-the-art performance comparing with other semi-supervised methods.

Hierarchical Consistency and Refinement for Semi-supervised Medical Segmentation

Satellite imagery is becoming increasingly available due to a large number of commercial satellite companies. Many fields use satellite images, including meteorology, forestry, natural disaster analysis, and agriculture. These images can be changed or tampered with image manipulation tools causing issues in applications using these images. Manipulation detection techniques designed for images captured by “consumer cameras” tend to fail when used on satellite images. In this paper we propose a supervised method, known as Nested Attention U-Net, to detect spliced areas in the satellite images. We introduce three datasets of manipulated satellite images that contain objects generated by a generative adversarial network (GAN). We test our approach and compare it to existing supervised splicing detection and segmentation techniques and show that our proposed approach performs well in detection and localization.

Nested Attention U-Net (NAU-N): A Splicing Detection Method for Satellite Images

We investigate the negative effects of rain streaks over the performance of a neural network for real time semantic segmentation of street scenes. This is done by synthetically augmenting the CityScapes dataset with artificial rain. We then define and train a generative adversarial network for rain removal, and quantify the benefits of its application as a pre-processing step to both rainy and "clean" images. Finally, we show that by retraining the semantic segmentation network on images processed for rain removal, it is possible to gain even more accuracy, with a model that produces stable results in all analyzed atmospheric conditions. For our experiments, we present a per-class analysis in order to provide deeper insights over the impact of rain on semantic segmentation.

On the impact of rain over semantic segmentation of street scenes

We propose a novel method for training Convolution Neural Network, named CNN-FQ, which takes a face image and outputs a scalar summary of the image quality. The CNN-FQ is trained from triplets of faces that are automatically labeled based on responses of a pre-trained face matcher. The quality scores extracted by the CNN-FQ are directly linked to the probability that the face matcher incorrectly ranks a randomly selected triplet of faces. We applied the proposed CNN-FQ, trained on CASIA database, for selection of the best quality image from a collection of face images capturing the same identity. The quality of the single face representation was evaluated on 1:1 Verification and 1:N Identification tasks defined by the challenging IJB-B protocol. We show that the recognition performance obtained when using faces selected based on the CNN-FQ scores is significantly higher than what can be achieved by competing state-of-the-art image quality extractors.

CNN Based Predictor of Face Image Quality

Low-light is a challenging environment for both human and computer vision to perform tasks such as object classification and detection. Recent works have shown potential in employing enhancements algorithms to support and improve such tasks in low-light, however there has not been any focused analysis to understand the direct effects that low-light enhancement have on an object detector. This work aims to quantify and visualize such effects on the multi-level abstractions involved in network-based object detection. First, low-light image enhancement algorithms are employed to enhance real low-light images, and then followed by deploying an object detection network on the low-light as well as the enhanced counterparts. A comparison of the activations in different layers, representing the detection features, are used to generate statistics in order to quantify the enhancements' contribution to detection. Finally, this framework was used to analyze several low-light image enhancement algorithms and identify their impact on the detection
model and task. This framework can also be easily generalized to any convolutional neural network-based models for the analysis of different enhancements algorithms and tasks.

Exploring the Contributions of Low-light Image Enhancement to Network-based Object Detection

This paper presents a deep learning-based method
to estimate gait attributes (e.g., stately, cool, relax, etc.). Similarly
to the existing studies on relative attribute, human perception-
based annotations on the gait attributes are given to pairs of
gait videos (i.e., the ﬁrst one is better, tie, and the second one
is better), and the relative annotations are utilized to train a
ranking model of the gait attribute. More speciﬁcally, we design
a Siamese (i.e., two-stream) network which takes a pair of
gait inputs and output gait attribute score for each. We then
introduce a suitable loss function called a signed contrastive loss
to train the network parameters with the relative annotation.
Unlike the existing loss functions for learning to rank does
not inherit a nice property of a quadratic contrastive loss, the
proposed signed quadratic contrastive loss function inherits the
nice property. The quantitative evaluation results reveal that
the proposed method shows better or comparable accuracies of
relative attribute prediction against the baseline methods.

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