Hybrid LBPH-inspired CNN for interactive web interface-based multi-face emotion recognition on embedded and IoT platforms

Abstract

Deploying real-time facial emotion recognition on internet of things (IoT) nodes, embedded boards, and reconfigurable-computing platforms is difficult, since conventional deep convolutional neural networks (CNNs) are usually too heavy, computationally, for such hardware. This work proposes a hybrid, local binary patterns histograms (LBPH)-inspired CNN pipeline: an LBPH style illumination-normalization stage is paired with a compact, purpose-built convolutional network, keeping memory and compute demands low enough to later port to embedded and field-programmable gate array (FPGA) targets. A Streamlit-based web interface prototypes the pipeline, supporting multi face detection with live histogram display. Tested on FER2013, the model reaches 68.5% accuracy ahead of plain LBPH's 58.4%, and close to much deeper CNNs while needing only 11.5 MB of memory (2.9 MB once int8 quantized) and 22 ms of central processing unit (CPU) inference time per face. An input-resolution ablation study identifies 96×96 as the best accuracy latency trade-off. Beyond a single accuracy number, per-class metrics are reported, along with a discussion of where the model is likely to confuse similar emotions and a layer-by-layer breakdown of parameters and multiply accumulate operations, intended to be directly useful for later hardware synthesis work.