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Researcher profile

Peter Corcoran

Peter Corcoran contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioneess.IVeess.ASSoundComputation and LanguageCryptography and Securitycs.CYDatabasesEmerging TechnologiesMachine LearningNeural and Evolutionary Computing

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Published work

11 published item(s)

preprint2026arXiv

From Review to Design: Ethical Multimodal Driver Monitoring Systems for Risk Mitigation, Incident Response, and Accountability in Automated Vehicles

As vehicles transition toward higher levels of automation, Driver Monitoring Systems (DMS) have become essential for ensuring human oversight, safety, and regulatory compliance in a vehicle. These systems rely on multimodal sensing and AI-driven inference to assess driver attention, cognitive state, and readiness to take control. While technologically promising, their deployment introduces a complex set of ethical and legal challenges - ranging from privacy and consent to data ownership and algorithmic fairness. While overarching frameworks such as the GDPR, EU AI Act, and IEEE standards offer important guidance, they lack the specificity required for addressing the unique risks posed by in-cabin sensing technologies. This paper adopts a review-to-design perspective, critically examining existing regulatory instruments and ethical frameworks -- such as the GDPR, the EU AI Act, and IEEE guidelines -- and identifying gaps in their applicability to the distinctive risks posed by multimodal, AI-enabled in-cabin monitoring. Building on this review, we propose a modular ethical design framework tailored specifically to Driver Monitoring Systems. The framework translates high-level principles into actionable design and deployment guidance, including user-configurable consent mechanisms, fairness-aware model development, transparency and explainability tools, and safeguards for driver emotional well-being. Finally, the paper outlines a risk analysis and failure mitigation strategy, emphasizing proactive incident response and accountability mechanisms tailored to the DMS context. Together, these contributions aim to inform the development of transparent, trustworthy, and human-centered driver monitoring systems for next-generation autonomous vehicles.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices

In this research work, we have proposed a thermal tiny-YOLO multi-class object detection (TTYMOD) system as a smart forward sensing system that should remain effective in all weather and harsh environmental conditions using an end-to-end YOLO deep learning framework. It provides enhanced safety and improved awareness features for driver assistance. The system is trained on large-scale thermal public datasets as well as newly gathered novel open-sourced dataset comprising of more than 35,000 distinct thermal frames. For optimal training and convergence of YOLO-v5 tiny network variant on thermal data, we have employed different optimizers which include stochastic decent gradient (SGD), Adam, and its variant AdamW which has an improved implementation of weight decay. The performance of thermally tuned tiny architecture is further evaluated on the public as well as locally gathered test data in diversified and challenging weather and environmental conditions. The efficacy of a thermally tuned nano network is quantified using various qualitative metrics which include mean average precision, frames per second rate, and average inference time. Experimental outcomes show that the network achieved the best mAP of 56.4% with an average inference time/ frame of 4 milliseconds. The study further incorporates optimization of tiny network variant using the TensorFlow Lite quantization tool this is beneficial for the deployment of deep learning architectures on the edge and mobile devices. For this study, we have used a raspberry pi 4 computing board for evaluating the real-time feasibility performance of an optimized version of the thermal object detection network for the automotive sensor suite. The source code, trained and optimized models and complete validation/ testing results are publicly available at https://github.com/MAli-Farooq/Thermal-YOLO-And-Model-Optimization-Using-TensorFlowLite.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Event-based YOLO Object Detection: Proof of Concept for Forward Perception System

Neuromorphic vision or event vision is an advanced vision technology, where in contrast to the visible camera that outputs pixels, the event vision generates neuromorphic events every time there is a brightness change which exceeds a specific threshold in the field of view (FOV). This study focuses on leveraging neuromorphic event data for roadside object detection. This is a proof of concept towards building artificial intelligence (AI) based pipelines which can be used for forward perception systems for advanced vehicular applications. The focus is on building efficient state-of-the-art object detection networks with better inference results for fast-moving forward perception using an event camera. In this article, the event-simulated A2D2 dataset is manually annotated and trained on two different YOLOv5 networks (small and large variants). To further assess its robustness, single model testing and ensemble model testing are carried out.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis

Speech synthesis has come a long way as current text-to-speech (TTS) models can now generate natural human-sounding speech. However, most of the TTS research focuses on using adult speech data and there has been very limited work done on child speech synthesis. This study developed and validated a training pipeline for fine-tuning state-of-the-art (SOTA) neural TTS models using child speech datasets. This approach adopts a multi-speaker TTS retuning workflow to provide a transfer-learning pipeline. A publicly available child speech dataset was cleaned to provide a smaller subset of approximately 19 hours, which formed the basis of our fine-tuning experiments. Both subjective and objective evaluations were performed using a pretrained MOSNet for objective evaluation and a novel subjective framework for mean opinion score (MOS) evaluations. Subjective evaluations achieved the MOS of 3.95 for speech intelligibility, 3.89 for voice naturalness, and 3.96 for voice consistency. Objective evaluation using a pretrained MOSNet showed a strong correlation between real and synthetic child voices. Speaker similarity was also verified by calculating the cosine similarity between the embeddings of utterances. An automatic speech recognition (ASR) model is also used to provide a word error rate (WER) comparison between the real and synthetic child voices. The final trained TTS model was able to synthesize child-like speech from reference audio samples as short as 5 seconds.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Smart speaker design and implementation with biometric authentication and advanced voice interaction capability

Advancements in semiconductor technology have reduced dimensions and cost while improving the performance and capacity of chipsets. In addition, advancement in the AI frameworks and libraries brings possibilities to accommodate more AI at the resource-constrained edge of consumer IoT devices. Sensors are nowadays an integral part of our environment which provide continuous data streams to build intelligent applications. An example could be a smart home scenario with multiple interconnected devices. In such smart environments, for convenience and quick access to web-based service and personal information such as calendars, notes, emails, reminders, banking, etc, users link third-party skills or skills from the Amazon store to their smart speakers. Also, in current smart home scenarios, several smart home products such as smart security cameras, video doorbells, smart plugs, smart carbon monoxide monitors, and smart door locks, etc. are interlinked to a modern smart speaker via means of custom skill addition. Since smart speakers are linked to such services and devices via the smart speaker user's account. They can be used by anyone with physical access to the smart speaker via voice commands. If done so, the data privacy, home security and other aspects of the user get compromised. Recently launched, Tensor Cam's AI Camera, Toshiba's Symbio, Facebook's Portal are camera-enabled smart speakers with AI functionalities. Although they are camera-enabled, yet they do not have an authentication scheme in addition to calling out the wake-word. This paper provides an overview of cybersecurity risks faced by smart speaker users due to lack of authentication scheme and discusses the development of a state-of-the-art camera-enabled, microphone array-based modern Alexa smart speaker prototype to address these risks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages

Technology-assisted platforms provide reliable solutions in almost every field these days. One such important application in the medical field is the skin cancer classification in preliminary stages that need sensitive and precise data analysis. For the proposed study the Kaggle skin cancer dataset is utilized. The proposed study consists of two main phases. In the first phase, the images are preprocessed to remove the clutters thus producing a refined version of training images. To achieve that, a sharpening filter is applied followed by a hair removal algorithm. Different image quality measurement metrics including Peak Signal to Noise (PSNR), Mean Square Error (MSE), Maximum Absolute Squared Deviation (MXERR) and Energy Ratio/ Ratio of Squared Norms (L2RAT) are used to compare the overall image quality before and after applying preprocessing operations. The results from the aforementioned image quality metrics prove that image quality is not compromised however it is upgraded by applying the preprocessing operations. The second phase of the proposed research work incorporates deep learning methodologies that play an imperative role in accurate, precise and robust classification of the lesion mole. This has been reflected by using two state of the art deep learning models: Inception-v3 and MobileNet. The experimental results demonstrate notable improvement in train and validation accuracy by using the refined version of images of both the networks, however, the Inception-v3 network was able to achieve better validation accuracy thus it was finally selected to evaluate it on test data. The final test accuracy using state of art Inception-v3 network was 86%.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Dataset Cleaning -- A Cross Validation Methodology for Large Facial Datasets using Face Recognition

In recent years, large "in the wild" face datasets have been released in an attempt to facilitate progress in tasks such as face detection, face recognition, and other tasks. Most of these datasets are acquired from webpages with automatic procedures. As a consequence, noisy data are often found. Furthermore, in these large face datasets, the annotation of identities is important as they are used for training face recognition algorithms. But due to the automatic way of gathering these datasets and due to their large size, many identities folder contain mislabeled samples which deteriorates the quality of the datasets. In this work, it is presented a semi-automatic method for cleaning the noisy large face datasets with the use of face recognition. This methodology is applied to clean the CelebA dataset and show its effectiveness. Furthermore, the list with the mislabelled samples in the CelebA dataset is made available.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generating Thermal Image Data Samples using 3D Facial Modelling Techniques and Deep Learning Methodologies

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

High-Accuracy Facial Depth Models derived from 3D Synthetic Data

In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a sequence of image frames together with ground truth depth and additional metadata such as head pose, light direction, etc. The synthetic data is used to train a CNN based facial depth estimation system which is validated on both synthetic and real images. Potential fields of application include 3D reconstruction, driver monitoring systems, robotic vision systems, and advanced scene understanding.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets

StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples. In this paper, we recap the StyleGAN architecture and training methodology and present our experiences of retraining it on a number of alternative public datasets. Practical issues and challenges arising from the retraining process are discussed. Tests and validation results are presented and a comparative analysis of several different re-trained StyleGAN weightings is provided 1. The role of this tool in building large, scalable datasets of synthetic facial data is also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Unconstrained Palmprint Recognition on Consumer Devices: a Literature Review

As a biometric palmprints have been largely under-utilized, but they offer some advantages over fingerprints and facial biometrics. Recent improvements in imaging capabilities on handheld and wearable consumer devices have re-awakened interest in the use fo palmprints. The aim of this paper is to provide a comprehensive review of state-of-the-art methods for palmprint recognition including Region of Interest extraction methods, feature extraction approaches and matching algorithms along with overview of available palmprint datasets in order to understand the latest trends and research dynamics in the palmprint recognition field.

0 citations0 reviewsNo code linkedOpen access