Abstract: FACE is one of the major sources of social information like race, age, gender etc. At different levels of classification, prediction and identification face plays a major role, apart from other parts of the human body. As per literature Race is a form of classification for categorizing human beings in to groups based on geographic boundaries, physical appearances(including face), ethnicity and social status. In this paper we are trying to focus on different facial datasets those are currently available without any cost (but with licensing restrictions). Here, we are also representing our study of different works carried-out related with the racial classification and related topics.FERET. The two experiments were carried-out using ANN and CNN respectively. In the 1 st experiment, they (i) calculated Geometric Features, (ii) extracted Skin Color and (iii) calculated Normalized Forehead Area. For the network, they used, out of 447 images (from FERET), 320 were used for training, 37 for validation and 97 for testing. In the 2 nd experiment, they used a pre-trained model [37] for the extraction of features from the samples (both training and testing). The used Network consisted of 13 convolution layers. After performing the feature extraction, training and testing was performed. The result of the experiments showed superiority of CNN over ANN as the accuracies were 98.6% and 82.4% respectively irrespective of costs. It also showed that time taken for extraction of features and training of the network by CNN is more than ANN. VI. CONCLUSIONWe divided our survey in two parts: (i) about the different recognized facial datasets and the (ii) about different procedures and methods adopted by various researchers from feature selection to race and ethnicity classification. From the survey, it is noticed that the face can precisely give the race. The survey shows that in the last few years, lots of researches are carried out and which basically signifies a tremendous progress in the learning process of race from facial images. In this survey we tried to provides, more or less, all the existing facial image datasets and also a comprehensive review of the advances in race.