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This study presents a novel, robust gait recognition algorithm for human identification from a sequence of segmented noisy silhouettes in a low-resolution video. The proposed recognition algorithm enables automatic human recognition from model-based gait cycle extraction based on the prediction-based hierarchical active shape model (ASM). The proposed algorithm overcomes drawbacks of existing works by extracting a set of relative model parameters instead of directly analysing the gait pattern. The feature extraction function in the proposed algorithm consists of motion detection, object region detection and ASM, which alleviate problems in the baseline algorithm such as background generation, shadow removal and higher recognition rate. Performance of the proposed algorithm has been evaluated by using the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition and time.