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Head-related transfer functions (HRTFs) describe the propagation of sound waves from the sound source to ear drums, which contain most of information for localization. However, HRTFs are highly individual-dependent and thus because of the difference of anthropometric features between subjects, individualization of HRTFs is a great challenge for accurate localization perception in virtual auditory displays (VAD). In this paper, we propose a sparsity-constrained weight mapping method termed SWM to obtain individual HRTFs. The key idea behind SWM is to obtain optimal weights to combine HRTFs from the training subjects based on the relationship of anthropometric features between the target subject and the training subjects. To this end, SWM learns two sparse representations between the target subject and the training subjects in terms of anthropometric features and HRTFs, respectively. A non-negative sparse model is used for this purpose when considering the non-negative property of the anthropometric features. Then, we build a mapping between the two weight vectors using a nonlinear regression. Furthermore, an iterative data extension method is proposed in order to increase training samples for mapping model. The objective and subjective experimental results show that the proposed method outperforms other methods in terms of log-spectral distortion (LSD) and localization accuracy.