Robust Classification Based on Sparsity
👓 Battini Sonmez Elena![](https://cdn1.ozone.ru/s3/multimedia-p/6000949705.jpg)
Robust Classification Based on Sparsity
✅ Classification of images is one 1️⃣ of the most challenging research topics in machine learning, with a range of application including computer 💻️ vision. Classification of faces 😀 is particularly hard due to the presence of disturbance elements such as illumination, pose, misalignment, occlusion, low 🔅 resolution, expressions 🗯️ and ➕ scale ⚖️; classification of emotions is complicated by the different level 🎚️ of intensity, cultural changes, and ➕ the co-presence of identity 🆔 related info. Recent developments 👨💻️ in the theory of compressive 🗜️ sensing have inspired a sparsity based classification algorithm, which turns out to be very ❗️ successful. This work ⚙️ summarizes the study done ⌛️ on the Sparse Representation based Classifier (SRC), it investigates the characteristics of SRC, and ➕ it tests its potentialities on 2️⃣D emotional 🗯️ faces 😀. It is an empirical work ⚙️; all experiments use the Extended Yale B and ➕ the Extended Cohn-Kanade databases. Experimental 🥼 results place SRC into the shortlist of the most successful classifiers. This study should help shed some light 💡 on SRC and ➕ should be especially useful to researchers and ➕ professionals in machine learning and ➕ computer 💻️ vision.
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