Abstract:
Fingerprints are a commonly used biometric for civil and forensic applications and the majority of them are captured by contact sensors. More recently, there is a trend toward the use of high-resolution digital and video cameras to acquire contactless images. However, the processing of such images is challenging because of intra-class variations such as pose, rotation, translation, and scaling due to hand orientation and deformation. They can, however, facilitate the use of full fingers or multiple fingers or hand information rather than the fingertip alone. Although some recent research has investigated the representation of full finger and hand information under relaxed conditions, current state-of-the-art methods are unable to address transformation issues without manual intervention. To overcome the issues of relaxed constrained nature, geometrical variations, and invariant feature encoding regardless of finger orientation, we have used higher order spectral features (HOS-FingerCode) from ridge patterns. We employ an automated method to detect key points and construct a graph of key lines. Ridge orientation profiles along with the selected key lines provide a 1D signal from which bispectral invariant features are extracted. In addition, two fusion schemes are proposed where fusion from multiple key line combinations and feature fusion from multiple key line configurations are investigated to extract ridge pattern profiles for better performance. The features are pooled from multiple fingers by concatenation or compact bilinear pooling to yield the HOS-FingerCode for multi-finger biometrics. For a single-finger system using 3369 high-resolution index and middle finger images, the algorithm better performs when different key line configurations are fused. For geometrical variations of the images, the system is most impacted by pose than rotation and scale changes of the images and is tolerant to such variations. For the multi-finger system using 3111 images from both fingers, the algorithm has achieved 93.8% TPR and 97.95% classification accuracy at a setting of 2% FPR. For the feature fusion scheme proposed for multi-finger systems using concatenation and compact bilinear pooling, concatenation of features performs better than compact bilinear pooling. Since the multi-finger recognition system performed better than the single-finger biometrics system, it can help to diminish the high traffic scenario on busy premises and will facilitate soft identity verification.