2024-01-10 コロンビア大学
◆学部生であるGabe Guo率いるチームは、約60,000の指紋を公共のデータベースから取得し、異なる指でも同一人物のものとしてAIに学習させました。AIは、従来の信念に反して異なる指からの指紋も一致させることができ、特に複数の指紋が提供された場合、現行の法科学の効率を10倍以上向上させる可能性があります。
<関連情報>
- https://www.engineering.columbia.edu/news/ai-discovers-not-every-fingerprint-unique
- https://www.science.org/doi/10.1126/sciadv.adi0329
深層対比学習により指紋の個人内類似性を解明 Unveiling intra-person fingerprint similarity via deep contrastive learning
Gabe Guo,Aniv Ray,Miles Izydorczak,Judah Goldfeder,Hod Lipson,and Wenyao Xu
Science Advances Published:12 Jan 2024
DOi:https://doi.org/10.1126/sciadv.adi0329
Abstract
Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.
