Merge pull request akshitagupta15june#848 from NEERAJAP2001/random_fo…

…rest Added Face Recognition using Random forest
sahillihas20 · Jun 5, 2021 · c82c0c1 · c82c0c1
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diff --git a/.../Face Detection using Random forest/10.03-Using-Random-Forests-for-Face-Recognition.ipynb b/.../Face Detection using Random forest/10.03-Using-Random-Forests-for-Face-Recognition.ipynb
diff --git a/Face-Detection/Face Detection using Random forest/README.md b/Face-Detection/Face Detection using Random forest/README.md
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+## Random Forest : 
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+Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.
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+## Using Random Forests for Face Recognition
+A popular dataset that we haven't talked much about yet is the Olivetti face dataset.
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+The Olivetti face dataset was collected in 1990 by AT&T Laboratories Cambridge. The dataset comprises facial images of 40 distinct subjects, taken at different times and under different lighting conditions. In addition, subjects varied their facial expression (open/closed eyes, smiling/not smiling) and their facial details (glasses/no glasses).
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+Images were then quantized to 256 grayscale levels and stored as unsigned 8-bit integers. Because there are 40 distinct subjects, the dataset comes with 40 distinct target labels. Recognizing faces thus constitutes an example of a multiclass classification task.
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+## Output : 
+![okay](https://user-images.githubusercontent.com/65017645/120203131-035eab80-c245-11eb-840a-f7a08e65a75a.png)