Introduction
Security systems worldwide use hand images for biometric identification, ensuring access to sensitive data and restricted spaces. However, forensic evidence involving hands often lacks the controlled quality seen in security systems, making it harder to analyze. In light of this, researchers from Lancaster University, including esteemed forensic anthropologist Sue Black, explored the potential of hand biometrics for robust identification, especially in sexual assault cases where other biometrics might not be available.
The Role of Hand Biometrics in Forensics
Hands offer unique biometric traits with less variability than faces, making them valuable for personal identification. Features like vein patterns provide distinct markers that remain stable over time, adding to their forensic utility.
The study notes that hand information is particularly relevant in scenarios where traditional biometrics, such as facial recognition or fingerprintsFingerprint, impression made by the papillary ridges on the ends of the fingers and thumbs. Fingerprints afford an infallible means of personal identification, because the ridge arrangement on every finger of every human being is unique and does not alter with growth or age. Fingerprints serve to reveal an individual’s true identity despite personal denial, assumed names, or changes in personal appearance resulting from age, disease, plastic surgery, or accident. The practice of utilizing fingerprints as a means of identification, referred to as dactyloscopy, is an indispensable aid to modern law enforcement. More, are unavailable due to environmental or technical challenges.
Introducing GPA-Net: A Novel Framework
The research team developed Global and Part-Aware Network (GPA-Net), a machine learning model tailored for analyzing hand biometrics. The innovation lies in treating right and left hands as separate datasets to account for their distinct vein patterns, a departure from prior studies that treated both hands as identical.
Dataset and Analysis
- 11k Hands Dataset (2016): A collaborative dataset with data from 190 subjects, sourced from Canada and Egypt.
- Hong Kong Polytechnic University Hand Dorsal (HD) Dataset: Comprising data from 502 subjects.
These datasets were further divided into four subsets:
- Right dorsal
- Left dorsal
- Right palmar
- Left palmar
This categorization ensured more accurate training and analysis, avoiding errors arising from comparing dissimilar hand types.
Performance of GPA-Net
The team benchmarked GPA-Net against two leading machine learning frameworks, ResNet50 and VGG19-bn, demonstrating its superiority in identifying individuals based on hand images:
- AccuracyIn scientific and measurement contexts, "accuracy" refers to the degree of proximity or closeness between a measured value and the true or actual value of the measured quantity. Accuracy indicates how well a measurement reflects the correct value. Here are key points about accuracy: • True Value: Accuracy assesses how closely a measurement or reading corresponds to the true, known, or accepted value of the quantity being measured. It is a measure of correctness.
• Error Measurement: The degree of accuracy is often expressed in terms of measurement error, which is the difference between the measured value and the true value. An accurate measurement has a minimal error.
• High Accuracy: A measurement or instrument is considered highly accurate when its readings are very close to the true value, with minimal or negligible error.
• Precision vs. Accuracy: Accuracy should not be confused with precision. Precision relates to the reproducibility and consistency of measurements. While accuracy addresses correctness, precision addresses how closely repeated measurements agree with each other.
• Example: If a laboratory balance measures the weight of a sample as 4.55 grams, and the actual weight of the sample is indeed 4.55 grams, the measurement is considered accurate.
• Error Sources: Errors in measurements can arise from various sources, including instrument calibration, environmental conditions, operator technique, and inherent limitations of the measurement device.
• Accuracy Assessment: To assess accuracy, calibration processes and standardization procedures are often employed to ensure that measurement instruments are correctly aligned with known reference standards.
• Quantitative Evaluation: Accuracy can be quantitatively evaluated by calculating the absolute or relative error, which expresses the difference between the measured value and the true value as a percentage or a fraction.
• Importance: In scientific research, quality control, manufacturing, and various fields, accuracy is essential for making informed decisions, ensuring product quality, and achieving reliable and credible results.
• Measurement Instruments: The accuracy of measurement instruments is a critical consideration in fields such as metrology, engineering, chemistry, and physics, where precise and accurate measurements are vital.
• Verification and Validation: To ensure the accuracy of measurements and instruments, verification and validation processes are often carried out, including testing and comparing results against reference standards.
In summary, accuracy in measurement refers to the degree of closeness between a measured value and the true or actual value of the quantity being measured. It is a fundamental concept in scientific research, quality control, and various industries where precise and reliable measurements are essential for making informed decisions and ensuring the quality and integrity of processes and products. More:- GPA-Net surpassed ResNet50 by 25%.
- GPA-Net outperformed VGG19-bn by 40%.
- Mean Average Precision (mAP):
- GPA-Net achieved a 38% improvement in precision over both competing models.
The findings highlight GPA-Net’s robust potential for precise identification, even in challenging forensic scenarios.
Applications in Criminal Investigations
Hand biometrics offer promising applications in forensic investigations, especially for cases involving sexual assaults or crimes where other biometric markers are absent. The researchers emphasize that hands, as primary biometric identifiers, could provide critical evidence through vein pattern recognition and other distinctive traits.
Conclusion
The study by Lancaster University underscores the transformative potential of hand biometrics in forensics. The GPA-Net framework, with its innovative approach to dataset segregation and superior performance metrics, marks a significant advancement in biometric identification. This research paves the way for more reliable, technology-driven solutions to aid forensic investigations and bolster security systems.
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Paper Reference
Baisa, Nathanael L. & Jiang, Zheheng & Vyas, Ritesh & Williams, Bryan & Rahmani, Hossein & Angelov, Plamen & Black, Sue. (2021). Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning. arXiv:2101.05260
