Automatic gender recognition has been becoming very important in potential applications. Many state-of-the-art gender recognition approaches based on a variety of biometrics, such as face, body shape, voice, are proposed recently. Among them, relying on voice is suboptimal due to significant variations in pitch, emotion, and noise in real-world speech. Inspired from the speaker recognition approaches relying on i-vector presentation in NIST SRE, it's believed that i-vector contains information about gender as a part of speaker's characters, and works for speaker recognition as well as for gender recognition in complex environments. So, we apply the total variability space analysis to gender classification and propose i-vector based discrimination for speaker gender recognition. The results of experiments on TIMIT corpus and NUST603_2014 database show that the proposed i-vector based speaker gender recognition improves the performance up to 99.9%, and surpasses the pitch method and UBM-SVM baseline subsystems in term of accuracy comparatively.