Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of a new image representation and the classification. The first phase comprises image binarization process and energy image generation using gait average image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event and our proposed method also outperforms Dalal Method
