ChatGPT Meets Iris Biometrics
- URL: http://arxiv.org/abs/2408.04868v1
- Date: Fri, 9 Aug 2024 05:13:07 GMT
- Title: ChatGPT Meets Iris Biometrics
- Authors: Parisa Farmanifard, Arun Ross,
- Abstract summary: This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition.
We investigate how well AI tools like ChatGPT can understand and analyze iris images.
Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions.
- Score: 10.902536447343465
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition - a field less common and more specialized than face recognition. By focusing on this niche yet crucial area, we investigate how well AI tools like ChatGPT can understand and analyze iris images. Through a series of meticulously designed experiments employing a zero-shot learning approach, the capabilities of ChatGPT-4 was assessed across various challenging conditions including diverse datasets, presentation attacks, occlusions such as glasses, and other real-world variations. The findings convey ChatGPT-4's remarkable adaptability and precision, revealing its proficiency in identifying distinctive iris features, while also detecting subtle effects like makeup on iris recognition. A comparative analysis with Gemini Advanced - Google's AI model - highlighted ChatGPT-4's better performance and user experience in complex iris analysis tasks. This research not only validates the use of LLMs for specialized biometric applications but also emphasizes the importance of nuanced query framing and interaction design in extracting significant insights from biometric data. Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions.
Related papers
- MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models [115.16022378880376]
We introduce a multimodal retrieval-augmented generation benchmark, MRAG-Bench.
MRAG-Bench consists of 16,130 images and 1,353 human-annotated multiple-choice questions.
Results show that all large vision-language models (LVLMs) exhibit greater improvements when augmented with images compared to textual knowledge.
arXiv Detail & Related papers (2024-10-10T17:55:02Z) - GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing [74.68232970965595]
Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos.
This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks.
arXiv Detail & Related papers (2024-03-09T13:56:25Z) - ChatGPT and biometrics: an assessment of face recognition, gender
detection, and age estimation capabilities [2.537406035246369]
We specifically examine the capabilities of ChatGPT in performing biometric-related tasks, with an emphasis on face recognition, gender detection, and age estimation.
Our study reveals that ChatGPT recognizes facial identities and differentiates between two facial images with considerable accuracy.
arXiv Detail & Related papers (2024-03-05T13:41:25Z) - Gemini vs GPT-4V: A Preliminary Comparison and Combination of
Vision-Language Models Through Qualitative Cases [98.35348038111508]
This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision)
The core of our analysis delves into the distinct visual comprehension abilities of each model.
Our findings illuminate the unique strengths and niches of both models.
arXiv Detail & Related papers (2023-12-22T18:59:58Z) - GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition [38.2581985358104]
GPT-4 with Vision (GPT-4V) has demonstrated remarkable visual capabilities across various tasks, but its performance in emotion recognition has not been fully evaluated.
We present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks.
arXiv Detail & Related papers (2023-12-07T13:27:37Z) - GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition? [82.40761196684524]
This paper centers on the evaluation of GPT-4's linguistic and visual capabilities in zero-shot visual recognition tasks.
We conduct extensive experiments to evaluate GPT-4's performance across images, videos, and point clouds.
Our findings show that GPT-4, enhanced with rich linguistic descriptions, significantly improves zero-shot recognition.
arXiv Detail & Related papers (2023-11-27T11:29:10Z) - Human Gait Recognition using Deep Learning: A Comprehensive Review [1.6085408991305155]
Gait recognition (GR) is a growing biometric modality used for person identification from a distance through visual cameras.
GR provides a secure and reliable alternative to fingerprint and face recognition, as it is harder to distinguish between false and authentic signals.
As video surveillance becomes more prevalent, new obstacles arise, such as ensuring uniform performance evaluation across different protocols, reliable recognition despite shifting lighting conditions, fluctuations in gait patterns, and protecting privacy.
arXiv Detail & Related papers (2023-09-18T20:47:57Z) - Multimodal Adaptive Fusion of Face and Gait Features using Keyless
attention based Deep Neural Networks for Human Identification [67.64124512185087]
Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification.
We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
arXiv Detail & Related papers (2023-03-24T05:28:35Z) - Learning Common Rationale to Improve Self-Supervised Representation for
Fine-Grained Visual Recognition Problems [61.11799513362704]
We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes.
We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
arXiv Detail & Related papers (2023-03-03T02:07:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.