Table 1-Suplthe GSL 60-Word Lexicon and Its Intra-Similarity Percentage

Table 1-Supl The GSL 60-word lexicon and its intra-similarity percentage wID word PS (%) wID word PS (%) wID word PS (%) 1 airplane 52.08 21 pillow 45.90 41 a lot 51.11 2 car 43.93 22 knife 55.38 42 glass 61.67 3 hug 50.59 23 big 55.47 43 scissors 56.42 4 bus 41.23 24 small 40.20 44 watch 54.56 5 teacher 40.02 25 mobile 49.46 45 workbook 47.94 6 read 43.14 26 pencil 48.04 46 desk 50.45 7 room 45.73 27 motorbike 51.69 47 table 50.72 8 road 58.64 28 bottle 55.06 48 train 50.70 9 rubber 55.44 29 closet 52.54 49 run 54.91 10 office 49.80 30 toothpaste 51.44 50 eat 54.59 11 write 46.49 31 toothbrush 51.71 51 bag 51.38 12 couch 44.05 32 window 50.95 52 TV 48.20 13 chair 50.93 33 police patrol 53.06 53 fireplace 55.53 14 mirror 51.93 34 walk 50.40 54 vase 52.64 15 pan 41.84 35 towel 58.46 55 book 54.82 16 chalk 52.76 36 play 55.28 56 library 54.32 17 bed 51.80 37 plate 54.70 57 tap 54.82 18 lamp 51.12 38 fork 59.03 58 paper 54.69 19 few 48.76 39 ship 51.10 59 draw 49.83 20 cook 37.04 40 bicycle 55.31 60 telephone 55.41 wID=Word ID; PS=Percentage of Similarity.

1 opt opt Table 2-Supl The estimated mi and m(i,l) values providing with the maximum recognition performance for each channel and for each signer and signer-pair

opt opt mi m(i,l) i (i,l) Ch 1 2 3 (1,2) (1,3) (2,3) 1 1,2,3 1,2,3,4 1,2,3,4,7 1,2,3 1,2,3,4 1,2,3,4,7 2 1,2,3,7 1,2,3,7 1,2,3 1,2,3,7 1,2,3,5 1,2,3,5 3 1,2,3 1,2,3,4 1,2,3,5 1,2,4 1,2,3 1,2,3,4 4 1,2,3,6 1,3,4,7 1,2,3,4 1,2,3,6 1,2,7 1,2,4,5 5 1,2,3,4,7 1,2,3,6 1,2,5,6 1,2,3,4,7 1,2,3,4,7 1,2,4,6 6 1,2,3,5,6 1,2,3,4,7 1,2,5,6 1,2,3,5,6 1,2,4,5,7 1,2,3,5,6 7 1,2,4 1,3,5 1,4,7 1,2,5 1,2,5 1,5,7 8 1,2,6 1,2,5 1,2,6 1,6,7 2,4 1,6,7 max max CAcci (%) CAcc(i,l) (%) 96.4 99.3 98.3 83.0 78.6 83.6 opt i and l denote the signer; denotes the channel number; mi : Optimum values of m parameter per single- max opt signer i, CAcci : Maximum classification accuracy for single-signer i (STEP 2a); m(i,l) : Optimum values of m parameter per signer-pair (i,l);: Maximum classification accuracy for signer-pair (i,l) (STEP 2b).

2 Table 3-Supl Mean classification accuracy of SampEn analysis for the signs with high PS

i H  CAcc j  std (%)

i 1 2 3 j 1 2 3 1 2 3 1 2 3 96.67 86.67 99.67 98.33 95. 67 99.67 98 88 99.33 8 ±17.36 ±26.73 ±4.71 ±8.7 ±11.27 ±3.33 ±11.4 ±26.53 ±6.45 79 100 96.67 92 100 98.67 79.33 100 97.33 9 ±31.05 ±0 ±14.57 ±14.31 ±0 ±6.56 ±33.67 ±0 ±13.10 81.33 99 65.67 91.67 99.67 79.67 80.67 100 71.67 22 ±30 ±8.12 ±14.72 ±14.51 ±3.33 ±19.47 ±3.32 ±0 ±37.47 95 100 66.33 96 100 83.33 88 100 66.33 23 wID ±21.07 ±0 ±14.8 ±13.63 ±0 ±22.47 ±26.53 ±0 ±42.79 98.33 99.67 78 99 99.67 91 100 89.67 79 35 ±10.43 ±4.71 ±39.6 ±5.71 ±3.33 ±20.02 ±0 ±26.81 ±31.76 29 100 89.67 63.67 99.67 95 25 100 91.67 38 ±32.96 ±0 ±25.09 ±26.42 ±3.33 ±11.96 ±50.43 ±0 ±22.10 53.67 81.67 67.67 81.67 88.67 79.67 52 81 65 42 ±31.04 ±29.84 ±37.79 ±23.39 ±15.87 ±20.04 ±48.29 ±30.17 ±40.07 (i,l) H  CAcc j  std (%) (i,l) (1,2) (1,3) (2,3) j 1 2 3 1 2 3 1 2 3 97 90 99 97.33 91 98.67 99 91 98.33 8 ±9.59 ±17.41 ±5.71 ±11.29 ±16.31 ±6.56 ±5.71 ±16.31 ±7.30 88.67 100 97.33 86 100 98.33 89 100 98 9 ±18.48 ±0 ±9.09 ±17.84 ±0 ±7.30 ±15.75 ±0 ±7.96 88.67 99.67 82.33 92.33 100 78 89 100 83 22 ±15.87 ±3.33 ±20.35 ±14.10 ±0 ±20.22 ±15.75 ±0 ±19.82 96.33 100 86 96.67 99.67 84.67 98 100 81.33 23 wID ±10.48 ±0 ±17.2 ±11.11 ±3.33 ±21.93 ±7.96 ±0 ±23.84 99 98 87 99.67 97.33 88.67 99.67 76.67 92.33 35 ±5.71 ±7.96 ±18.29 ±3.33 ±10.25 ±17.87 ±3.33 ±31.96 ±16.31 72.33 100 93.67 72.33 100 91 74.67 100 94 38 ±24.64 ±0 ±13.14 ±24.64 ±0 ±14.87 ±26.85 ±0 ±12.87 79 93 81.67 80.67 92.67 83.33 81 91 81.33 42 ±25.80 ±13.65 ±17.96 ±20.75 ±13.88 ±19.25 ±23.33 ±14.87 ±20.28 i i,j and l denote the signer; wID=Word ID; H  CAcc j : Mean classification accuracy for words with high (i,l) PS for signer j using ; H  CAcc j : Mean classification accuracy for words with high PS for signer j opt using m(i,l) .