In order to improve the accuracy of the human pose estimation task of convolutional neural networks, we propose an improved loss function based on Mean Squared Error (MSE) to deal with the pixel imbalance between foreground (Gaussian kernel) and background in heatmaps, assign different weights to the loss function according to different pixel values in the foreground and background, and named it Focus Mean Squared Error (FMSE). Compared with the mean squared loss function, the proposed focused mean squared loss function can effectively reduce the impact of pixel imbalance between foreground and background on network performance, help the network locate the spatial location of key points, improve network performance, and make the loss function converge faster in the training phase. Experiments are performed on public data sets to verify the effectiveness of the proposed focused mean square loss function.