In this paper, we focus on the template inversion attack against face recognition systems and propose a new method to reconstruct face images from facial templates. Within a generative adversarial network (GAN)-based framework, we learn a mapping from facial templates to the intermediate latent space of a pre-trained face generation network, from which we can generate high-resolution realistic reconstructed face images. We show that our proposed method can be applied in whitebox and blackbox attacks against face recognition systems. Furthermore, we evaluate the transferability of our attack when the adversary uses the reconstructed face image to impersonate the underlying subject in an attack against another face recognition system. Considering the adversary's knowledge and the target face recognition system, we define five different attacks and evaluate the vulnerability of state-of-the-art face recognition systems. Our experiments show that our proposed method achieves high success attack rates in whitebox and blackbox scenarios. Furthermore, the reconstructed face images are transferable and can be used to enter target face recognition systems with a different feature extractor model. We also explore important areas in the reconstructed face images that can fool the target face recognition system.

Neural Information Processing Systems http://nips.cc/

An advanced emotion classification model was developed using a CNN-Transformer architecture for emotion recognition from EEG brain wave signals, effectively distinguishing among three emotional states, positive, neutral and negative. The model achieved a testing accuracy of 91%, outperforming traditional models such as SVM, DNN, and Logistic Regression. Training was conducted on a custom dataset created by merging data from SEED, SEED-FRA, and SEED-GER repositories, comprising 1,455 samples with EEG recordings labeled according to emotional states. The combined dataset represents one of the largest and most culturally diverse collections available. Additionally, the model allows for the reduction of the requirements of the EEG apparatus, by leveraging only 5 electrodes of the 62. This reduction demonstrates the feasibility of deploying a more affordable consumer-grade EEG headset, thereby enabling accessible, at-home use, while also requiring less computational power. This advancement sets the groundwork for future exploration into mood changes induced by media content consumption, an area that remains underresearched. Integration into medical, wellness, and home-health platforms could enable continuous, passive emotional monitoring, particularly beneficial in clinical or caregiving settings where traditional behavioral cues, such as facial expressions or vocal tone, are diminished, restricted, or difficult to interpret, thus potentially transforming mental health diagnostics and interventions...

Place recognition--the process of an animal, person or robot recognizing a familiar location in the world--has attracted significant attention across multiple disciplines. In animals, this capability has evolved over millions of years through sophisticated neural mechanisms: hippocampal place cells fire at specific spatial locations (1), entorhinal grid cells provide spatial coordinates through hexagonal firing patterns (2), while diverse species demonstrate remarkable navigation--from desert ants using celestial cues and visual panoramas (3) to migratory birds returning to precise breeding sites across hemispheric distances (4). Humans extend these biological foundations with unique cognitive abilities, recognizing places not only through sensory perception but also through semantic meaning, emotional associations, and cultural context--enabling us to identify familiar locations from descriptions, memories, or even fictional narratives (5). In artificial systems, place recognition underpins core robotics functions such as localization, mapping, and long-term autonomy, developing into a mature field that, while sometimes inspired by biological principles, often diverges significantly in implementation to optimize for computational efficiency and metric accuracy. As research has grown in the area, so too has a rich landscape of surveys and reviews that reflect the field's evolution and diversification.

A.1 Architecture We provide additional details of the V oiceBlock architecture used in our experiments. ReLU activations and hidden size 512. Bottleneck: Our bottleneck consists of two LSTM layers with hidden size 512. We implement a version of V oiceBlock for processing audio streams in real time. Lookahead Buffer: Contains overlapping frames to be used as lookahead.

By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring.

Human activity recognition is an important research topic in pattern recognition field. It has been the subject of many studies in the past two decades because of its importance in numerous areas such as security, health, daily activity, energy consumption and robotics. Recently, some works on the recognition of hand gestures or human actions from skeletal data are based on the modeling of the skeleton's movement as manifold-based representation and proposed deep neural networks on this structure [1, 2, 3]. These approaches demonstrated their potential in the processing of skeletal data. Most of them are applied on offline human action recognition which is useful in time-limited tasks. However, in many applications, simply recognizing a single gesture in a given segmented sequence is not enough, especially in monitoring systems and virtual-reality devices which need to detect human movements moment by moment in continuous videos. In these online recognition systems, it is important to detect the existence of an action as early as possible after its beginning. It is also essential to determine the nature of the movement within a sequence of frames, without having information about the number of gestures present within the video, their starting times or their durations, unlike the segmented action recognition. In this paper, we propose to use a manifold-based model in order to build an online motion recognition system that detects and identifies different human activities in unsegmented skeletal sequences.

Developing a highly accurate automatic license plate recognition system (ALPR) is challenging due to environmental factors such as lighting, rain, and dust. Additional difficulties include high vehicle speeds, varying camera angles, and low-quality or low-resolution images. ALPR is vital in traffic control, parking, vehicle tracking, toll collection, and law enforcement applications. This paper proposes a deep learning strategy using YOLOv8 for license plate detection and recognition tasks. This method seeks to enhance the performance of the model using datasets from Ontario, Quebec, California, and New York State. It achieved an impressive recall rate of 94% on the dataset from the Center for Pattern Recognition and Machine Intelligence (CENPARMI) and 91% on the UFPR-ALPR dataset. In addition, our method follows a semi-supervised learning framework, combining a small set of manually labeled data with pseudo-labels generated by Grounding DINO to train our detection model. Grounding DINO, a powerful vision-language model, automatically annotates many images with bounding boxes for license plates, thereby minimizing the reliance on labor-intensive manual labeling. By integrating human-verified and model-generated annotations, we can scale our dataset efficiently while maintaining label quality, which significantly enhances the training process and overall model performance. Furthermore, it reports character error rates for both datasets, providing additional insight into system performance.

When Face Recognition Doesn't Know Your Face Is a Face An estimated 100 million people live with facial differences. As face recognition tech becomes widespread, some say they're getting blocked from accessing essential systems and services. Autumn Gardiner thought updating her driving license would be straightforward. After getting married last year, she headed to the local Department of Motor Vehicles office in Connecticut to get her name changed on her license. While she was there, Gardiner recalls, officials said she needed to update her photo.

By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring.