Nowadays, most of the people alters/or conceals their true facial appearance intentionally and/or un-intentionally by wearing various disguise accessories such as sunglasses, artificial beard and moustache, face make-up, and many more fancy items. Since, these accessories obscures the prominent facial features, the traditional face recognition systems have not shown a notable recognition performance and thus its performance is challengeable in the various applications fields such as immigration and border control, national security, surveillance, and many more. In literature, IIIT-DDFD, IMFDB, and FDB are disguise datasets are available for Indian ethnicity. Our analysis indicates that, these datasets are not sufficient with enough facial samples to meet the current trends. Therefore, we have introduced an Indian celebrity disguise face dataset (ICDFD), which includes the samples with wide range of complex disguise variations combined with pose, illumination, and expression. Initially, we analyze the performance of these datasets using holistic approaches and followed by deep learning models. From the experimental analysis, it reveals that the deep learning models have shown an optimal performance over holistic approaches. It is observed that, the disguised faces are continue to pose wide open challenges for the researchers in the area of computer vision.