Processed 29,051 images ensuring balanced emotion distribution, achieving performance through dimensionality reduction and advanced recommendation algorithm integration. Implemented NLP techniques for lyrics analysis to predict user preferences through the integration of collaborative filtering, content-based filtering, and deep learning models.
For facial expression detection, MobileNetV2, ResNet50, VGG16, InceptionV3, Xception, and EfficientNetB7 are chosen for their effectiveness in capturing detailed facial features and efficiently processing complex images. These architectures, available in tensorflow.keras.applications, can be fine-tuned for facial expression detection to ensure high accuracy and performance in real-time applications.
- Efficiency and Lightweight Design: MobileNetV2's depthwise separable convolutions significantly reduce parameters and computational load, making it ideal for mobile and embedded devices with limited resources.
- Residual Learning: ResNet50 uses residual blocks to mitigate the vanishing gradient problem, enabling deeper networks to learn intricate features essential for accurate facial expression detection.
- Simplicity and Depth: VGG16 employs deep layers composed of small 3x3 convolutions, facilitating effective feature extraction crucial for capturing subtle facial expression variations.
- Multi-Scale Feature Extraction: InceptionV3 utilizes inception modules with multiple filter sizes in parallel, capturing a wide range of features and enhancing facial expression detection.
- Efficiency through Depthwise Separable Convolutions: Xception's use of depthwise separable convolutions reduces computational cost while maintaining high performance, making it powerful and efficient for detailed image analysis.
- State-of-the-Art Performance: EfficientNetB7 uses compound scaling to uniformly scale width, depth, and resolution, achieving leading accuracy on benchmarks with optimal parameter efficiency.