Biometric discretization converts extracted biometric features into a binary string via a process of segmenting every one-dimensional feature space into possibly distinct multiple intervals and encoding every interval-captured feature element correspondingly. Eventually, the individual binary output of every feature element is concatenated into a binary string. To the best of our knowledge, Detection Rate Optimized Bit Allocation (DROBA) scheme is currently the most effective biometric quantization scheme, offering its capability in assigning bits dynamically for each user-specific feature. However, we discover that DROBA suffers from potential discriminative feature miss-detections and under-quantized conditions. This paper highlights such drawbacks and improves upon DROBA by incorporating a dynamic searching method to efficiently recapture such miss-detected features. Experimental results illustrating significant improvements in classification accuracy justify the practicality of our approach.