Fast fingerprint feature extraction based on modified Haar-like patterns using support vector machine

• Published in: 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) pp. 429 - 430
• Main Authors: Yi-Pin Hsu, Yen-Lin Chen, Chen-Fu Liao, Xiu-Zhi Chen, Chao-Wei Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2017
• DOI: 10.1109/ICCE-China.2017.7991179

In this paper, Haar-like patterns and a classification machine were employed for fingerprint feature extraction. Although the Gabor filter has high accuracy for feature extraction under a wide angle representation range, the full range of the filter is not required. Moreover, fingerprints have clear direction field representation. Due to these two key factors, this paper uses modified Haar-like patterns to create near-circular patterns for acceptable feature detection. A support vector machine classifier is used to detect features. In a performance comparison that used 15 general Haar-like patterns as a benchmark, the proposed algorithm was able to reduce the average computation required by approximately 50% without sacrificing accuracy. Thus, the proposed method is suitable for real-world applications.