Aiming to address the problems of scale uncertainty, feature extraction difficulty, model training difficulty, poor real-time performance, and sample imbalance in low-resolution images for gender recognition, this study proposes an efficient pedestrian gender recognition model based on key area feature extraction and fusion. First, a discrete cosine transform (DCT)-based local super-resolution preprocessing algorithm is developed for facial image gender recognition. Then, a key area feature extraction and information fusion model is designed, using additional appearance features to assist in gender recognition and improve accuracy. The proposed model preprocesses images using the DCT image fusion and super-resolution methods, dividing pedestrian images into three regions: face, hair, and lower body (legs) regions. Features are separately extracted from each of the three image regions. Finally, a multi-region local gender recognition classifier is designed and trained, employing decision-level information fusion. The results of the three local classifiers are fused using a Bayesian computation-based fusion strategy to obtain the final recognition result of a pedestrian’s gender. This study uses surveillance video data to create a dataset for experimental comparison. Experimental results demonstrate the superiority of the proposed approach. The facial model (DCT-PFSR-CNN) achieved the best accuracy of 89% and an F1-Score of 0.88. Furthermore, the complete pedestrian model (MPGRM) attained an mAP of 0.85 and an AUC of 0.86, surpassing the strongest baseline (HDFL) by 2.4% in mAP and 2.3% in AUC. These results confirm the high application potential of the proposed method for gender recognition in real-world surveillance scenarios.