Table 2.

Quantitative comparison among deep learning, various handcrafted and ensemble feature extraction methods (Classifier wise recognition accuracy (in %))

Features	Gaussian Naïve Bayes	Decision tree	Random forest	XGB Classifier
F1	55.37	54.49	57.47	63.13
F1 + F2	67.65	68.51	70.31	73.02
F1 + F3	64.69	63.52	66.79	71.59
F1 + F4	71.84	72.62	75.08	78.29
F1 + F5	70.37	70.93	72.88	76.64
F1 + F2 + F3	75.04	77.29	76.63	80.38
F1 + F2 + F4	80.71	80.93	82.24	83.74
F1 + F2 + F5	78.01	78.07	79.48	82.76
F1 + F3 + F4	78.46	80.58	81.01	84.16
F1 + F3 + F5	75.03	77.05	77.31	83.00
F1 + F4 + F5	81.77	83.68	84.88	84.55
F1 + F2 + F3 + F4	86.32	88.47	89.65	88.86
F1 + F2 + F3 + F5	82.69	83.90	84.00	87.87
F1 + F2 + F4 + F5	89.16	89.29	90.23	90.35
F1 + F3 + F4 + F5	85.63	88.33	89.42	88.84
F1 + F2 + F3 + F4 + F5	92.05	92.67	93.73	93.02

Bold face of text depicting the maximum accuracy achieved in each table