A security index for biometric systems is essential because biometrics have been widely adopted as a secure authentication component in critical systems. Most of bio-metric systems secured by template protection schemes are based on binary templates. To adopt popular template protection schemes such as fuzzy commitment and fuzzy extractor that can be applied on binary templates only, non-binary templates (e.g., real-valued, point-set based) need to be converted to binary. However, existing security measurements for binary template based biometric systems either cannot reflect the actual attack difficulties or are too computationally expensive to be practical. This paper presents an acceleration of the guessing entropy which reflects the expected number of guessing trials in attacking the binary template based biometric systems. The acceleration benefits from computation reuse and pruning. Experimental results on two datasets show that the acceleration has more than 6x, 20x, and 200x speed up without losing the estimation accuracy in different system settings.