Predicting facial beauty in computer vision is a relatively emerging research area with diverse applications. However, Facial Beauty Prediction (FBP) has various challenging possibilities due to the lack of a universally accepted assessment procedure for facial beauty, the scarcity of sufficient available databases and computational models, and the way of extracting discriminative features to quantify facial attractiveness. This paper comprehensively reviews the 2D facial image beauty analysis and prediction research that utilizes computation, machine, and deep learning techniques. It introduces the limitations and makes a critical analysis of this research area. Beauty hypotheses, feature extraction, evaluation methods, and FBP benchmark datasets that can help measure the effectiveness of the automatic attractiveness assessment approaches are discussed and analyzed. In addition, this paper tries to figure out the answer to the most debatable question that says, “which face organs contribute to facial beauty and to what extent?”. It also highlights concerns and challenges in the FBP domain that can provide a foundation for future work and further development in automatic facial beauty estimation and evaluation.

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