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The emotional context of a given environment can profoundly influence an individual’s feelings and responses. Nonetheless, current emotion recognition methodologies primarily concentrate on analyzing the target subject’s features and inadequately integrate these features with the contextual information of the scene. To tackle this challenge, we introduce a novel emotion recognition model that employs three independent and prioritized deep convolutional neural networks, alongside a feature fusion enhancement technique, to effectively merge facial information, body pose information, and subject features within the overall image. By amalgamating the performance of object detection models and deep convolutional network models, our framework capitalizes on the strengths of multiple approaches. Experiments with the Emotic dataset validate that our proposed model is technically innovative and surpasses existing methods and benchmark models in terms of feature fusion performance. Moreover, our evaluation of the proposed method on the Emotic dataset underscores the significance of environmental contextual information in shaping human emotions.