Utilizing the full-field Digital Image Correlation (DIC) measurement technique for inverse modeling proves advantageous in calibrating the constitutive behavior of materials subjected to both monotonic and fatigue loading conditions. The challenge arises in fatigue analysis, characterized by a substantial number of cycles, posing difficulties for DIC implementation due to potential big data issues. The problems encompass the extensive capturing and collection of frames over numerous cycles, along with the subsequent postprocessing required for thorough DIC analysis. Consequently, the calibration of cyclic constitutive models using DIC becomes inherently challenging. To address this challenge, an intermittent approach is implemented, wherein DIC frames are systematically segregated at the peaks and valleys of cycles under constant amplitude loading. The model response is then fitted to the DIC measurements. However, the intermittent nature of the data introduces a potential source of error, necessitating a quantitative assessment of the error to validate the accuracy of this approach. This research adopts an intermittent scheme for image selection in performing inverse modelling using DIC. Finite Element Method simulations are executed to numerically predict the strain distribution, providing a basis for comparison with DIC results. The comparisons show that intermittent technique can provide accurate results and enable the use of DIC for fatigue analysis.