Sparse Locally Adaptive Regression Kernel For Face Verification

Abstract The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime. The proposed method has significantly higher influence when the verification process is computationally intensive. The proposed approach requires minimal training and face verification is accomplished based on the threshold value obtained during training. The whole process is modest and nearly all of the time spent is solely on quantifying any of the distance metrics between the LARKs obtained from the two faces for verification. LARK descriptors compute a measure of resemblance on the basis of “signal-induced distance” between a pixel and its nearby pixels. We assess the interspace between the LARKs from these faces and analogize the resemblance from the threshold resulting in a binary decision.